Publications by authors named "Angelo Maria Sabatini"

33 Publications

Exoskeletons for workers: A case series study in an enclosures production line.

Appl Ergon 2022 May 20;101:103679. Epub 2022 Jan 20.

The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, 56025, Pontedera, Pisa, Italy; Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 33, 56127, Pisa, Italy; IRCCS Fondazione Don Carlo Gnocchi, 50143, Florence, Italy. Electronic address:

This case-series study aims to investigate the effects of a passive shoulder support exoskeleton on experienced workers during their regular work shifts in an enclosures production site. Experimental activities included three sessions, two of which were conducted in-field (namely, at two workstations of the painting line, where panels were mounted and dismounted from the line; each session involved three participants), and one session was carried out in a realistic simulated environment (namely, the workstations were recreated in a laboratory; this session involved four participants). The effect of the exoskeleton was evaluated through electromyographic activity and perceived effort. After in-field sessions, device usability and user acceptance were also assessed. Data were reported individually for each participant. Results showed that the use of the exoskeleton reduced the total shoulder muscular activity compared to normal working conditions, in all subjects and experimental sessions. Similarly, the use of the exoskeleton resulted in reductions of the perceived effort in the shoulder, arm, and lower back. Overall, participants indicated high usability and acceptance of the device. This case series invites larger validation studies, also in diverse operational contexts.
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http://dx.doi.org/10.1016/j.apergo.2022.103679DOI Listing
May 2022

Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing.

Sensors (Basel) 2021 Sep 21;21(18). Epub 2021 Sep 21.

Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy.

The orientation of a magneto-inertial measurement unit can be estimated using a sensor fusion algorithm (SFA). However, orientation accuracy is greatly affected by the choice of the SFA parameter values which represents one of the most critical steps. A commonly adopted approach is to fine-tune parameter values to minimize the difference between estimated and true orientation. However, this can only be implemented within the laboratory setting by requiring the use of a concurrent gold-standard technology. To overcome this limitation, a Rigid-Constraint Method (RCM) was proposed to estimate suboptimal parameter values without relying on any orientation reference. The RCM method effectiveness was successfully tested on a single-parameter SFA, with an average error increase with respect to the optimal of 1.5 deg. In this work, the applicability of the RCM was evaluated on 10 popular SFAs with multiple parameters under different experimental scenarios. The average residual between the optimal and suboptimal errors amounted to 0.6 deg with a maximum of 3.7 deg. These encouraging results suggest the possibility to properly tune a generic SFA on different scenarios without using any reference. The synchronized dataset also including the optical data and the SFA codes are available online.
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http://dx.doi.org/10.3390/s21186307DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473403PMC
September 2021

Analysis of the Accuracy of Ten Algorithms for Orientation Estimation Using Inertial and Magnetic Sensing under Optimal Conditions: One Size Does Not Fit All.

Sensors (Basel) 2021 Apr 5;21(7). Epub 2021 Apr 5.

PolitoBIOMed Lab-Biomedical Engineering Lab and Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy.

The orientation of a magneto and inertial measurement unit (MIMU) is estimated by means of sensor fusion algorithms (SFAs) thus enabling human motion tracking. However, despite several SFAs implementations proposed over the last decades, there is still a lack of consensus about the best performing SFAs and their accuracy. As suggested by recent literature, the filter parameters play a central role in determining the orientation errors. The aim of this work is to analyze the accuracy of ten SFAs while running under the best possible conditions (i.e., their parameter values are set using the orientation reference) in nine experimental scenarios including three rotation rates and three commercial products. The main finding is that parameter values must be specific for each SFA according to the experimental scenario to avoid errors comparable to those obtained when the default parameter values are used. Overall, when optimally tuned, no statistically significant differences are observed among the different SFAs in all tested experimental scenarios and the absolute errors are included between 3.8 deg and 7.1 deg. Increasing the rotation rate generally leads to a significant performance worsening. Errors are also influenced by the MIMU commercial model. SFA MATLAB implementations have been made available online.
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http://dx.doi.org/10.3390/s21072543DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8038545PMC
April 2021

Locomotory behaviour of the intertidal marble crab (Pachygrapsus marmoratus) supports the underwater spring-loaded inverted pendulum as a fundamental model for punting in animals.

Bioinspir Biomim 2020 07 29;15(5):055004. Epub 2020 Jul 29.

The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy. Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy. Department of Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Napoli, Italy.

In aquatic pedestrian locomotion the dynamics of terrestrial and aquatic environments are coupled. Here we study terrestrial running and aquatic punting locomotion of the marine-living crab Pachygrapsus marmoratus. We detected both active and passive phases of running and punting through the observation of crab locomotory behaviour in standardized settings and by three-dimensional kinematic analysis of its dynamic gaits using high-speed video cameras. Variations in different stride parameters were studied and compared. The comparison was done based on the dimensionless parameter the Froude number (Fr) to account for the effect of buoyancy and size variability among the crabs. The underwater spring-loaded inverted pendulum (USLIP) model better fitted the dynamics of aquatic punting. USLIP takes account of the damping effect of the aquatic environment, a variable not considered by the spring-loaded inverted pendulum (SLIP) model in reduced gravity. Our results highlight the underlying principles of aquatic terrestrial locomotion by comparing it with terrestrial locomotion. Comparing punting with running, we show and increased stride period, decreased duty cycle and orientation of the carapace more inclined with the horizontal plane, indicating the significance of fluid forces on the dynamics due to the aquatic environment. Moreover, we discovered periodicity in punting locomotion of crabs and two different gaits, namely, long-flight punting and short-flight punting, distinguished by both footfall patterns and kinematic parameters. The generic fundamental model which belongs to all animals performing both terrestrial and aquatic legged locomotion has implications for control strategies, evolution and translation to robotic artefacts.
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http://dx.doi.org/10.1088/1748-3190/ab968cDOI Listing
July 2020

Grasp force estimation from the transient EMG using high-density surface recordings.

J Neural Eng 2020 02 12;17(1):016052. Epub 2020 Feb 12.

Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy. The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy. Author to whom any correspondence should be addressed.

Objective: Understanding the neurophysiological signals underlying voluntary motor control and decoding them for prosthesis control are among the major challenges in applied neuroscience and bioengineering. Usually, information from the electrical activity of residual forearm muscles (i.e. the electromyogram, EMG) is used to control different functions of a prosthesis. Noteworthy, forearm EMG patterns at the onset of a contraction (transient phase) have shown to contain predictive information about upcoming grasps. However, decoding this information for the estimation of grasp force (GF) was so far overlooked.

Approach: High density-EMG signals (192 channels) were recorded from twelve participants performing a pick-and-lift task. The final GF was estimated offline using linear regressors, with four subsets of channels and ten features obtained using three channels-features selection methods. Two different evaluation metrics (absolute error and R ), complemented with statistical analysis, were used to select the optimal configuration of the parameters. Different windows of data starting at the GF onset were compared to determine the time at which the GF can be ascertained from the EMG signals.

Main Results: The prediction accuracy improved by increasing the window length from the moment of the onset and kept improving until the steady state at which a plateau of performances was reached. With our methodology, estimations of the GF through 16 EMG channels reached an absolute error of 2.52% the maximum voluntary force using only transient information and 1.99% with the first 500 ms of data following the onset.

Significance: The final GF estimation from transient EMG was comparable to the one obtained using steady state data, confirming our hypothesis that the transient phase contains information about the final GF. This result paves the way to fast online myoelectric controllers capable of decoding grasp strength from the very early portion of the EMG signal.
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http://dx.doi.org/10.1088/1741-2552/ab673fDOI Listing
February 2020

Tongue Rehabilitation Device for Dysphagic Patients.

Sensors (Basel) 2019 Oct 26;19(21). Epub 2019 Oct 26.

The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, 56025 Pontedera (PI), Italy.

Dysphagia refers to difficulty in swallowing often associated with syndromic disorders. In dysphagic patients' rehabilitation, tongue motility is usually treated and monitored via simple exercises, in which the tongue is pushed against a depressor held by the speech therapist in different directions. In this study, we developed and tested a simple pressure/force sensor device, named "Tonic Tongue (ToTo)", intended to support training and monitoring tasks for the rehabilitation of tongue musculature. It consists of a metallic frame holding a ball bearing support equipped with a sterile disposable depressor, whose angular displacements are counterbalanced by extensional springs. The conversion from angular displacement to force is managed using a simple mechanical model of ToTo operation. Since the force exerted by the tongue in various directions can be estimated, quantitative assessment of the outcome of a given training program is possible. A first prototype of ToTo was tested on 26 healthy adults, who were trained for one month. After the treatment, we observed a statistically significant improvement with a force up to 2.2 N (median value) in all tested directions of pushing, except in the downward direction, in which the improvement was slightly higher than 5 N (median value). ToTo promises to be an innovative and reliable device that can be used for the rehabilitation of dysphagic patients. Moreover, since it is a self-standing device, it could be used as a point-of-care solution for in-home rehabilitation management of dysphasia.
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http://dx.doi.org/10.3390/s19214657DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6865205PMC
October 2019

Combining Evolutionary and Adaptive Control Strategies for Quadruped Robotic Locomotion.

Front Neurorobot 2019 29;13:71. Epub 2019 Aug 29.

The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy.

In traditional robotics, model-based controllers are usually needed in order to bring a robotic plant to the next desired state, but they present critical issues when the dimensionality of the control problem increases and disturbances from the external environment affect the system behavior, in particular during locomotion tasks. It is generally accepted that the motion control of quadruped animals is performed by neural circuits located in the spinal cord that act as a Central Pattern Generator and can generate appropriate locomotion patterns. This is thought to be the result of evolutionary processes that have optimized this network. On top of this, fine motor control is learned during the lifetime of the animal thanks to the plastic connections of the cerebellum that provide descending corrective inputs. This research aims at understanding and identifying the possible advantages of using learning during an evolution-inspired optimization for finding the best locomotion patterns in a robotic locomotion task. Accordingly, we propose a comparative study between two bio-inspired control architectures for quadruped legged robots where learning takes place either during the evolutionary search or only after that. The evolutionary process is carried out in a simulated environment, on a quadruped legged robot. To verify the possibility of overcoming the reality gap, the performance of both systems has been analyzed by changing the robot dynamics and its interaction with the external environment. Results show better performance metrics for the robotic agent whose locomotion method has been discovered by applying the adaptive module during the evolutionary exploration for the locomotion trajectories. Even when the motion dynamics and the interaction with the environment is altered, the locomotion patterns found on the learning robotic system are more stable, both in the joint and in the task space.
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http://dx.doi.org/10.3389/fnbot.2019.00071DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6727738PMC
August 2019

Ambulatory Assessment of the Dynamic Margin of Stability Using an Inertial Sensor Network.

Sensors (Basel) 2019 Sep 23;19(19). Epub 2019 Sep 23.

The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127 Pisa, Italy.

Loss of stability is a precursor to falling and therefore represents a leading cause of injury, especially in fragile people. Thus, dynamic stability during activities of daily living (ADLs) needs to be considered to assess balance control and fall risk. The dynamic margin of stability (MOS) is often used as an indicator of how the body center of mass is located and moves relative to the base of support. In this work, we propose a magneto-inertial measurement unit (MIMU)-based method to assess the MOS of a gait. Six young healthy subjects were asked to walk on a treadmill at different velocities while wearing MIMUs on their lower limbs and pelvis. We then assessed the MOS by computing the lower body displacement with respect to the leading inverse kinematics approach. The results were compared with those obtained using a camera-based system in terms of root mean square deviation (RMSD) and correlation coefficient (ρ). We obtained a RMSD of ≤1.80 cm and ρ ≥ 0.85 for each walking velocity. The findings revealed that our method is comparable to camera-based systems in terms of accuracy, suggesting that it may represent a strategy to assess stability during ADLs in unstructured environments.
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http://dx.doi.org/10.3390/s19194117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6806087PMC
September 2019

Reducing Fall Risk with Combined Motor and Cognitive Training in Elderly Fallers.

Brain Sci 2017 Feb 10;7(2). Epub 2017 Feb 10.

Clinical and Behavioral Neurology Laboratory, IRCCS Fondazione Santa Lucia, Rome 00179, Italy.

Background: Falling is a major clinical problem in elderly people, demanding effective solutions. At present, the only effective intervention is motor training of balance and strength. Executive function-based training (EFt) might be effective at preventing falls according to evidence showing a relationship between executive functions and gait abnormalities. The aim was to assess the effectiveness of a motor and a cognitive treatment developed within the EU co-funded project I-DONT-FALL.

Methods: In a sample of 481 elderly people at risk of falls recruited in this multicenter randomised controlled trial, the effectiveness of a motor treatment (pure motor or mixed with EFt) of 24 one-hour sessions delivered through an -Walker with a non-motor treatment (pure EFt or control condition) was evaluated. Similarly, a 24 one-hour session cognitive treatment (pure EFt or mixed with motor training), delivered through a touch-screen computer was compared with a non-cognitive treatment (pure motor or control condition).

Results: Motor treatment, particularly when mixed with EFt, reduced significantly fear of falling (F(1,478) = 6.786, = 0.009) although to a limited extent (ES -0.25) restricted to the period after intervention.

Conclusions: This study suggests the effectiveness of motor treatment empowered by EFt in reducing fear of falling.
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http://dx.doi.org/10.3390/brainsci7020019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5332962PMC
February 2017

Automatic classification of gait in children with early-onset ataxia or developmental coordination disorder and controls using inertial sensors.

Gait Posture 2017 02 2;52:287-292. Epub 2016 Dec 2.

The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.

Early-Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) are two conditions that affect coordination in children. Phenotypic identification of impaired coordination plays an important role in their diagnosis. Gait is one of the tests included in rating scales that can be used to assess motor coordination. A practical problem is that the resemblance between EOA and DCD symptoms can hamper their diagnosis. In this study we employed inertial sensors and a supervised classifier to obtain an automatic classification of the condition of participants. Data from shank and waist mounted inertial measurement units were used to extract features during gait in children diagnosed with EOA or DCD and age-matched controls. We defined a set of features from the recorded signals and we obtained the optimal features for classification using a backward sequential approach. We correctly classified 80.0%, 85.7%, and 70.0% of the control, DCD and EOA children, respectively. Overall, the automatic classifier correctly classified 78.4% of the participants, which is slightly better than the phenotypic assessment of gait by two pediatric neurologists (73.0%). These results demonstrate that automatic classification employing signals from inertial sensors obtained during gait maybe used as a support tool in the differential diagnosis of EOA and DCD. Furthermore, future extension of the classifier's test domains may help to further improve the diagnostic accuracy of pediatric coordination impairment. In this sense, this study may provide a first step towards incorporating a clinically objective and viable biomarker for identification of EOA and DCD.
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http://dx.doi.org/10.1016/j.gaitpost.2016.12.002DOI Listing
February 2017

Ambulatory Assessment of Instantaneous Velocity during Walking Using Inertial Sensor Measurements.

Sensors (Basel) 2016 Dec 21;16(12). Epub 2016 Dec 21.

The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127 Pisa, Italy.

A novel approach for estimating the instantaneous velocity of the pelvis during walking was developed based on Inertial Measurement Units (IMUs). The instantaneous velocity was modeled by the sum of a cyclical component, decomposed in the Medio-Lateral (ML), VerTical (VT) and Antero-Posterior (AP) directions, and the Average Progression Velocity (APV) over each gait cycle. The proposed method required the availability of two IMUs, attached to the pelvis and one shank. Gait cycles were identified from the shank angular velocity; for each cycle, the Fourier series coefficients of the pelvis and shank acceleration signals were computed. The cyclical component was estimated by Fourier-based time-integration of the pelvis acceleration. A Bayesian Linear Regression (BLR) with Automatic Relevance Determination (ARD) predicted the APV from the stride time, the stance duration, and the Fourier series coefficients of the shank acceleration. Healthy subjects performed tasks of Treadmill Walking (TW) and Overground Walking (OW), and an optical motion capture system (OMCS) was used as reference for algorithm performance assessment. The widths of the limits of agreements (±1.96 standard deviation) were computed between the proposed method and the reference OMCS, yielding, for the cyclical component in the different directions: ML: ±0.07 m/s (±0.10 m/s); VT: ±0.03 m/s (±0.05 m/s); AP: ±0.06 m/s (±0.10 m/s), in TW (OW) conditions. The ARD-BLR achieved an APV root mean square error of 0.06 m/s (0.07 m/s) in the same conditions.
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http://dx.doi.org/10.3390/s16122206DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191184PMC
December 2016

Dealing with Magnetic Disturbances in Human Motion Capture: A Survey of Techniques.

Micromachines (Basel) 2016 Mar 9;7(3). Epub 2016 Mar 9.

The BioRobotics Institute, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, Pisa 56125, Italy.

Magnetic-Inertial Measurement Units (MIMUs) based on microelectromechanical (MEMS) technologies are widespread in contexts such as human motion tracking. Although they present several advantages (lightweight, size, cost), their orientation estimation accuracy might be poor. Indoor magnetic disturbances represent one of the limiting factors for their accuracy, and, therefore, a variety of work was done to characterize and compensate them. In this paper, the main compensation strategies included within Kalman-based orientation estimators are surveyed and classified according to which degrees of freedom are affected by the magnetic data and to the magnetic disturbance rejection methods implemented. By selecting a representative method from each category, four algorithms were obtained and compared in two different magnetic environments: (1) small workspace with an active magnetic source; (2) large workspace without active magnetic sources. A wrist-worn MIMU was used to acquire data from a healthy subject, whereas a stereophotogrammetric system was adopted to obtain ground-truth data. The results suggested that the model-based approaches represent the best compromise between the two testbeds. This is particularly true when the magnetic data are prevented to affect the estimation of the angles with respect to the vertical direction.
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http://dx.doi.org/10.3390/mi7030043DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6189838PMC
March 2016

Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-Inertial-Based Human Body Tracking.

Sensors (Basel) 2016 Jan 26;16(2):153. Epub 2016 Jan 26.

The BioRobotics Institute, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, 56125 Pisa, Italy.

Information from complementary and redundant sensors are often combined within sensor fusion algorithms to obtain a single accurate observation of the system at hand. However, measurements from each sensor are characterized by uncertainties. When multiple data are fused, it is often unclear how all these uncertainties interact and influence the overall performance of the sensor fusion algorithm. To address this issue, a benchmarking procedure is presented, where simulated and real data are combined in different scenarios in order to quantify how each sensor's uncertainties influence the accuracy of the final result. The proposed procedure was applied to the estimation of the pelvis orientation using a waist-worn magnetic-inertial measurement unit. Ground-truth data were obtained from a stereophotogrammetric system and used to obtain simulated data. Two Kalman-based sensor fusion algorithms were submitted to the proposed benchmarking procedure. For the considered application, gyroscope uncertainties proved to be the main error source in orientation estimation accuracy for both tested algorithms. Moreover, although different performances were obtained using simulated data, these differences became negligible when real data were considered. The outcome of this evaluation may be useful both to improve the design of new sensor fusion methods and to drive the algorithm tuning process.
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http://dx.doi.org/10.3390/s16020153DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4801531PMC
January 2016

A Simulation Environment for Benchmarking Sensor Fusion-Based Pose Estimators.

Sensors (Basel) 2015 Dec 19;15(12):32031-44. Epub 2015 Dec 19.

The BioRobotics Institute, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, Pisa 56125, Italy.

In-depth analysis and performance evaluation of sensor fusion-based estimators may be critical when performed using real-world sensor data. For this reason, simulation is widely recognized as one of the most powerful tools for algorithm benchmarking. In this paper, we present a simulation framework suitable for assessing the performance of sensor fusion-based pose estimators. The systems used for implementing the framework were magnetic/inertial measurement units (MIMUs) and a camera, although the addition of further sensing modalities is straightforward. Typical nuisance factors were also included for each sensor. The proposed simulation environment was validated using real-life sensor data employed for motion tracking. The higher mismatch between real and simulated sensors was about 5% of the measured quantity (for the camera simulation), whereas a lower correlation was found for an axis of the gyroscope (0.90). In addition, a real benchmarking example of an extended Kalman filter for pose estimation from MIMU and camera data is presented.
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http://dx.doi.org/10.3390/s151229903DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4721821PMC
December 2015

Fourier-based integration of quasi-periodic gait accelerations for drift-free displacement estimation using inertial sensors.

Biomed Eng Online 2015 Nov 23;14:106. Epub 2015 Nov 23.

The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio, 34, 56025, Pontedera, Pisa, Italy.

Background: In biomechanical studies Optical Motion Capture Systems (OMCS) are considered the gold standard for determining the orientation and the position (pose) of an object in a global reference frame. However, the use of OMCS can be difficult, which has prompted research on alternative sensing technologies, such as body-worn inertial sensors.

Methods: We developed a drift-free method to estimate the three-dimensional (3D) displacement of a body part during cyclical motions using body-worn inertial sensors. We performed the Fourier analysis of the stride-by-stride estimates of the linear acceleration, which were obtained by transposing the specific forces measured by the tri-axial accelerometer into the global frame using a quaternion-based orientation estimation algorithm and detecting when each stride began using a gait-segmentation algorithm. The time integration was performed analytically using the Fourier series coefficients; the inverse Fourier series was then taken for reconstructing the displacement over each single stride. The displacement traces were concatenated and spline-interpolated to obtain the entire trace.

Results: The method was applied to estimate the motion of the lower trunk of healthy subjects that walked on a treadmill and it was validated using OMCS reference 3D displacement data; different approaches were tested for transposing the measured specific force into the global frame, segmenting the gait and performing time integration (numerically and analytically). The width of the limits of agreements were computed between each tested method and the OMCS reference method for each anatomical direction: Medio-Lateral (ML), VerTical (VT) and Antero-Posterior (AP); using the proposed method, it was observed that the vertical component of displacement (VT) was within ±4 mm (±1.96 standard deviation) of OMCS data and each component of horizontal displacement (ML and AP) was within ±9 mm of OMCS data.

Conclusions: Fourier harmonic analysis was applied to model stride-by-stride linear accelerations during walking and to perform their analytical integration. Our results showed that analytical integration based on Fourier series coefficients was a useful approach to accurately estimate 3D displacement from noisy acceleration data.
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http://dx.doi.org/10.1186/s12938-015-0103-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4657361PMC
November 2015

How Angular Velocity Features and Different Gyroscope Noise Types Interact and Determine Orientation Estimation Accuracy.

Sensors (Basel) 2015 Sep 18;15(9):23983-4001. Epub 2015 Sep 18.

Interuniversity Center of Bioengineering of the Human Neuromusculoskeletal System, Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Piazza Lauro de Bosis 15, 00135 Roma, Italy.

In human movement analysis, 3D body segment orientation can be obtained through the numerical integration of gyroscope signals. These signals, however, are affected by errors that, for the case of micro-electro-mechanical systems, are mainly due to: constant bias, scale factor, white noise, and bias instability. The aim of this study is to assess how the orientation estimation accuracy is affected by each of these disturbances, and whether it is influenced by the angular velocity magnitude and 3D distribution across the gyroscope axes. Reference angular velocity signals, either constant or representative of human walking, were corrupted with each of the four noise types within a simulation framework. The magnitude of the angular velocity affected the error in the orientation estimation due to each noise type, except for the white noise. Additionally, the error caused by the constant bias was also influenced by the angular velocity 3D distribution. As the orientation error depends not only on the noise itself but also on the signal it is applied to, different sensor placements could enhance or mitigate the error due to each disturbance, and special attention must be paid in providing and interpreting measures of accuracy for orientation estimation algorithms.
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http://dx.doi.org/10.3390/s150923983DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4610477PMC
September 2015

Prior-to- and Post-Impact Fall Detection Using Inertial and Barometric Altimeter Measurements.

IEEE Trans Neural Syst Rehabil Eng 2016 07 30;24(7):774-83. Epub 2015 Jul 30.

This paper investigates a fall detection system based on the integration of an inertial measurement unit with a barometric altimeter (BIMU). The vertical motion of the body part the BIMU was attached to was monitored on-line using a method that delivered drift-free estimates of the vertical velocity and estimates of the height change from the floor. The experimental study included activities of daily living of seven types and falls of five types, simulated by a cohort of 25 young healthy adults. The downward vertical velocity was thresholded at 1.38 m/s, yielding 80% sensitivity (SE), 100% specificity (SP) and a mean prior-to-impact time of 157 ms (range 40-300 ms). The soft falls, i.e., those with downward vertical velocity above 0.55 m/s and below 1.38 m/s were analyzed post-impact. Six fall detection methods, tuned to achieve 100% SE, were considered to include features of impact, change of posture and height, singularly or in association with one another. No single feature allowed for 100% SP. The detection accuracy marginally improved when the height change was considered in association with either the impact or the change of posture; the post-impact fall detection method that analyzed the impact and the change of posture together achieved 100% SP.
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http://dx.doi.org/10.1109/TNSRE.2015.2460373DOI Listing
July 2016

Estimating orientation using magnetic and inertial sensors and different sensor fusion approaches: accuracy assessment in manual and locomotion tasks.

Sensors (Basel) 2014 Oct 9;14(10):18625-49. Epub 2014 Oct 9.

The BioRobotics Institute, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, 56124 Pisa, Italy.

Magnetic and inertial measurement units are an emerging technology to obtain 3D orientation of body segments in human movement analysis. In this respect, sensor fusion is used to limit the drift errors resulting from the gyroscope data integration by exploiting accelerometer and magnetic aiding sensors. The present study aims at investigating the effectiveness of sensor fusion methods under different experimental conditions. Manual and locomotion tasks, differing in time duration, measurement volume, presence/absence of static phases, and out-of-plane movements, were performed by six subjects, and recorded by one unit located on the forearm or the lower trunk, respectively. Two sensor fusion methods, representative of the stochastic (Extended Kalman Filter) and complementary (Non-linear observer) filtering, were selected, and their accuracy was assessed in terms of attitude (pitch and roll angles) and heading (yaw angle) errors using stereophotogrammetric data as a reference. The sensor fusion approaches provided significantly more accurate results than gyroscope data integration. Accuracy improved mostly for heading and when the movement exhibited stationary phases, evenly distributed 3D rotations, it occurred in a small volume, and its duration was greater than approximately 20 s. These results were independent from the specific sensor fusion method used. Practice guidelines for improving the outcome accuracy are provided.
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http://dx.doi.org/10.3390/s141018625DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4239903PMC
October 2014

Walking speed estimation using foot-mounted inertial sensors: comparing machine learning and strap-down integration methods.

Med Eng Phys 2014 Oct 5;36(10):1312-21. Epub 2014 Sep 5.

The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.

In this paper we implemented machine learning (ML) and strap-down integration (SDI) methods and analyzed them for their capability of estimating stride-by-stride walking speed. Walking speed was computed by dividing estimated stride length by stride time using data from a foot mounted inertial measurement unit. In SDI methods stride-by-stride walking speed estimation was driven by detecting gait events using a hidden Markov model (HMM) based method (HMM-based SDI); alternatively, a threshold-based gait event detector was investigated (threshold-based SDI). In the ML method a linear regression model was developed for stride length estimation. Whereas the gait event detectors were a priori fixed without training, the regression model was validated with leave-one-subject-out cross-validation. A subject-specific regression model calibration was also implemented to personalize the ML method. Healthy adults performed over-ground walking trials at natural, slower-than-natural and faster-than-natural speeds. The ML method achieved a root mean square estimation error of 2.0% and 4.2%, with and without personalization, against 2.0% and 3.1% by HMM-based SDI and threshold-based SDI. In spite that the results achieved by the two approaches were similar, the ML method, as compared with SDI methods, presented lower intra-subject variability and higher inter-subject variability, which was reduced by personalization.
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http://dx.doi.org/10.1016/j.medengphy.2014.07.022DOI Listing
October 2014

A sensor fusion method for tracking vertical velocity and height based on inertial and barometric altimeter measurements.

Sensors (Basel) 2014 Jul 24;14(8):13324-47. Epub 2014 Jul 24.

The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio, Pontedera 34 56025, Pisa, Italy.

A sensor fusion method was developed for vertical channel stabilization by fusing inertial measurements from an Inertial Measurement Unit (IMU) and pressure altitude measurements from a barometric altimeter integrated in the same device (baro-IMU). An Extended Kalman Filter (EKF) estimated the quaternion from the sensor frame to the navigation frame; the sensed specific force was rotated into the navigation frame and compensated for gravity, yielding the vertical linear acceleration; finally, a complementary filter driven by the vertical linear acceleration and the measured pressure altitude produced estimates of height and vertical velocity. A method was also developed to condition the measured pressure altitude using a whitening filter, which helped to remove the short-term correlation due to environment-dependent pressure changes from raw pressure altitude. The sensor fusion method was implemented to work on-line using data from a wireless baro-IMU and tested for the capability of tracking low-frequency small-amplitude vertical human-like motions that can be critical for stand-alone inertial sensor measurements. Validation tests were performed in different experimental conditions, namely no motion, free-fall motion, forced circular motion and squatting. Accurate on-line tracking of height and vertical velocity was achieved, giving confidence to the use of the sensor fusion method for tracking typical vertical human motions: velocity Root Mean Square Error (RMSE) was in the range 0.04-0.24 m/s; height RMSE was in the range 5-68 cm, with statistically significant performance gains when the whitening filter was used by the sensor fusion method to track relatively high-frequency vertical motions.
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http://dx.doi.org/10.3390/s140813324DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4179067PMC
July 2014

Online decoding of hidden Markov models for gait event detection using foot-mounted gyroscopes.

IEEE J Biomed Health Inform 2014 Jul;18(4):1122-30

In this paper, we present an approach to the online implementation of a gait event detector based on machine learning algorithms. Gait events were detected using a uniaxial gyro that measured the foot instep angular velocity in the sagittal plane to feed a four-state left-right hidden Markov model (HMM). The short-time Viterbi algorithm was used to overcome the limitation of the standard Viterbi algorithm, which does not allow the online decoding of hidden state sequences. Supervised learning of the HMM structure and validation with the leave-one-subject-out validation method were performed using treadmill gait reference data from an optical motion capture system. The four gait events were foot strike, flat foot (FF), heel off (HO), and toe off. The accuracy ranged, on average, from 45 ms (early detection, FF) to 35 ms (late detection, HO); the latency of detection was less than 100 ms for all gait events but the HO, where the probability that it was greater than 100 ms was 25%. Overground walking tests of the HMM-based gait event detector were also successfully performed.
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http://dx.doi.org/10.1109/JBHI.2013.2293887DOI Listing
July 2014

Pre-impact fall detection: optimal sensor positioning based on a machine learning paradigm.

PLoS One 2014 21;9(3):e92037. Epub 2014 Mar 21.

The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy; Translational Neural Engineering Lab, Center for Neuroprosthetics, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland.

The aim of this study was to identify the best subset of body segments that provides for a rapid and reliable detection of the transition from steady walking to a slipping event. Fifteen healthy young subjects managed unexpected perturbations during walking. Whole-body 3D kinematics was recorded and a machine learning algorithm was developed to detect perturbation events. In particular, the linear acceleration of all the body segments was parsed by Independent Component Analysis and a Neural Network was used to classify walking from unexpected perturbations. The Mean Detection Time (MDT) was 351±123 ms with an Accuracy of 95.4%. The procedure was repeated with data related to different subsets of all body segments whose variability appeared strongly influenced by the perturbation-induced dynamic modifications. Accordingly, feet and hands accounted for most data information and the performance of the algorithm were slightly reduced using their combination. Results support the hypothesis that, in the framework of the proposed approach, the information conveyed by all the body segments is redundant to achieve effective fall detection, and suitable performance can be obtained by simply observing the kinematics of upper and lower distal extremities. Future studies are required to assess the extent to which such results can be reproduced in older adults and in different experimental conditions.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0092037PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3962372PMC
January 2015

A stochastic approach to noise modeling for barometric altimeters.

Sensors (Basel) 2013 Nov 18;13(11):15692-707. Epub 2013 Nov 18.

The BioRobotics Institute, Scuola Superiore Sant'Anna/P.zza Martiri della Libertà, 33, 56124 Pisa, Italy.

The question whether barometric altimeters can be applied to accurately track human motions is still debated, since their measurement performance are rather poor due to either coarse resolution or drifting behavior problems. As a step toward accurate short-time tracking of changes in height (up to few minutes), we develop a stochastic model that attempts to capture some statistical properties of the barometric altimeter noise. The barometric altimeter noise is decomposed in three components with different physical origin and properties: a deterministic time-varying mean, mainly correlated with global environment changes, and a first-order Gauss-Markov (GM) random process, mainly accounting for short-term, local environment changes, the effects of which are prominent, respectively, for long-time and short-time motion tracking; an uncorrelated random process, mainly due to wideband electronic noise, including quantization noise. Autoregressive-moving average (ARMA) system identification techniques are used to capture the correlation structure of the piecewise stationary GM component, and to estimate its standard deviation, together with the standard deviation of the uncorrelated component. M-point moving average filters used alone or in combination with whitening filters learnt from ARMA model parameters are further tested in few dynamic motion experiments and discussed for their capability of short-time tracking small-amplitude, low-frequency motions.
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http://dx.doi.org/10.3390/s131115692DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3871085PMC
November 2013

Extended Kalman filter-based methods for pose estimation using visual, inertial and magnetic sensors: comparative analysis and performance evaluation.

Sensors (Basel) 2013 Feb 4;13(2):1919-41. Epub 2013 Feb 4.

The Institute of BioRobotics, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, Pisa, Italy.

In this paper measurements from a monocular vision system are fused with inertial/magnetic measurements from an Inertial Measurement Unit (IMU) rigidly connected to the camera. Two Extended Kalman filters (EKFs) were developed to estimate the pose of the IMU/camera sensor moving relative to a rigid scene (ego-motion), based on a set of fiducials. The two filters were identical as for the state equation and the measurement equations of the inertial/magnetic sensors. The DLT-based EKF exploited visual estimates of the ego-motion using a variant of the Direct Linear Transformation (DLT) method; the error-driven EKF exploited pseudo-measurements based on the projection errors from measured two-dimensional point features to the corresponding three-dimensional fiducials. The two filters were off-line analyzed in different experimental conditions and compared to a purely IMU-based EKF used for estimating the orientation of the IMU/camera sensor. The DLT-based EKF was more accurate than the error-driven EKF, less robust against loss of visual features, and equivalent in terms of computational complexity. Orientation root mean square errors (RMSEs) of 1° (1.5°), and position RMSEs of 3.5 mm (10 mm) were achieved in our experiments by the DLT-based EKF (error-driven EKF); by contrast, orientation RMSEs of 1.6° were achieved by the purely IMU-based EKF.
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http://dx.doi.org/10.3390/s130201919DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3649364PMC
February 2013

Variable-State-Dimension Kalman-based Filter for orientation determination using inertial and magnetic sensors.

Sensors (Basel) 2012 25;12(7):8491-506. Epub 2012 Jun 25.

The BioRobotics Institute, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, Pisa 56127, Italy.

In this paper a quaternion-based Variable-State-Dimension Extended Kalman Filter (VSD-EKF) is developed for estimating the three-dimensional orientation of a rigid body using the measurements from an Inertial Measurement Unit (IMU) integrated with a triaxial magnetic sensor. Gyro bias and magnetic disturbances are modeled and compensated by including them in the filter state vector. The VSD-EKF switches between a quiescent EKF, where the magnetic disturbance is modeled as a first-order Gauss-Markov stochastic process (GM-1), and a higher-order EKF where extra state components are introduced to model the time-rate of change of the magnetic field as a GM-1 stochastic process, namely the magnetic disturbance is modeled as a second-order Gauss-Markov stochastic process (GM-2). Experimental validation tests show the effectiveness of the VSD-EKF, as compared to either the quiescent EKF or the higher-order EKF when they run separately.
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http://dx.doi.org/10.3390/s120708491DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444060PMC
February 2013

Gait phase detection and discrimination between walking-jogging activities using hidden Markov models applied to foot motion data from a gyroscope.

Gait Posture 2012 Sep 15;36(4):657-61. Epub 2012 Jul 15.

The BioRobotics Institute, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 33, 56127 Pisa, Italy.

In this paper we present a classifier based on a hidden Markov model (HMM) that was applied to a gait treadmill dataset for gait phase detection and walking/jogging discrimination. The gait events foot strike, foot flat, heel off, toe off were detected using a uni-axial gyroscope that measured the foot instep angular velocity in the sagittal plane. Walking/jogging activities were discriminated by processing gyroscope data from each detected stride. Supervised learning of the classifier was undertaken using reference data from an optical motion analysis system. Remarkably good generalization properties were achieved across tested subjects and gait speeds. Sensitivity and specificity of gait phase detection exceeded 94% and 98%, respectively, with timing errors that were less than 20 ms, on average; the accuracy of walking/jogging discrimination was approximately 99%.
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http://dx.doi.org/10.1016/j.gaitpost.2012.06.017DOI Listing
September 2012

Estimating three-dimensional orientation of human body parts by inertial/magnetic sensing.

Sensors (Basel) 2011 26;11(2):1489-525. Epub 2011 Jan 26.

The BioRobotics Institute, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, 56124 Pisa, Italy.

User-worn sensing units composed of inertial and magnetic sensors are becoming increasingly popular in various domains, including biomedical engineering, robotics, virtual reality, where they can also be applied for real-time tracking of the orientation of human body parts in the three-dimensional (3D) space. Although they are a promising choice as wearable sensors under many respects, the inertial and magnetic sensors currently in use offer measuring performance that are critical in order to achieve and maintain accurate 3D-orientation estimates, anytime and anywhere. This paper reviews the main sensor fusion and filtering techniques proposed for accurate inertial/magnetic orientation tracking of human body parts; it also gives useful recipes for their actual implementation.
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http://dx.doi.org/10.3390/s110201489DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3274035PMC
June 2012

A hidden Markov model-based technique for gait segmentation using a foot-mounted gyroscope.

Annu Int Conf IEEE Eng Med Biol Soc 2011 ;2011:4369-73

The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.

In this paper, we describe an application of hidden Markov models (HMMs) to the problem of time-locating specific events in normal gait movement patterns. The use of HMMs in this paper is mainly related to the opportunity they offer to segment gait data collected at different walking speeds and inclinations of the walking surface. A simple four-state left-right HMM is trained on a dataset of signals collected from a mono-axial gyro during treadmill walking trials performed at different speed and incline values. The gyro is mounted at the foot instep, with its sensitivity axis oriented in the medio-lateral direction. A rule based method applied to gyro signals is used for data annotation. Sensitivity and specificity of phase classification detection higher than 95% are obtained. The estimation accuracy of heel strike, flat foot, heel off and toe off events is about 35 ms on average.
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http://dx.doi.org/10.1109/IEMBS.2011.6091084DOI Listing
July 2012

On-line classification of human activity and estimation of walk-run speed from acceleration data using support vector machines.

Annu Int Conf IEEE Eng Med Biol Soc 2011 ;2011:3302-5

The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.

The awareness of the physical activity that human subjects perform, and the quantification of activity strength and duration are important tasks that a wearable sensor system would fulfill to be valuable in several biomedical applications, from health monitoring to physical medicine and rehabilitation. In this work we develop a wearable sensor system that collect data from a single thigh-mounted tri-axial accelerometer; the system performs activity classification (sit, stand, cycle, walk, run), and speed estimation for walk (run) labeled data features. These classification/estimation tasks are achieved by cascading two Support Vector Machines (SVM) classifiers. Activity classification accuracy higher than 99% and root mean square errors E(RMS) = 0.28 km/h for speed estimation are obtained in our preliminary experiments. The developed wearable sensor system provides activity labels and speed point estimates at the pace of two readings per second.
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http://dx.doi.org/10.1109/IEMBS.2011.6090896DOI Listing
June 2012

Kalman-filter-based orientation determination using inertial/magnetic sensors: observability analysis and performance evaluation.

Sensors (Basel) 2011 27;11(10):9182-206. Epub 2011 Sep 27.

The BioRobotics Institute, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, 56124 Pisa, Italy.

In this paper we present a quaternion-based Extended Kalman Filter (EKF) for estimating the three-dimensional orientation of a rigid body. The EKF exploits the measurements from an Inertial Measurement Unit (IMU) that is integrated with a tri-axial magnetic sensor. Magnetic disturbances and gyro bias errors are modeled and compensated by including them in the filter state vector. We employ the observability rank criterion based on Lie derivatives to verify the conditions under which the nonlinear system that describes the process of motion tracking by the IMU is observable, namely it may provide sufficient information for performing the estimation task with bounded estimation errors. The observability conditions are that the magnetic field, perturbed by first-order Gauss-Markov magnetic variations, and the gravity vector are not collinear and that the IMU is subject to some angular motions. Computer simulations and experimental testing are presented to evaluate the algorithm performance, including when the observability conditions are critical.
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http://dx.doi.org/10.3390/s111009182DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231259PMC
June 2012
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