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Marco Caruso

Politecnico di Torino, Department of Electronics and Telecommunications, Graduate Student

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Loughborough University in London

Revista Ingenieria Industrial

Universidad del Bio-bio

Universidad Argentina de la Empresa

FacSenac

Universidad Nacional Autónoma de México

NTNU

Leonardo Reyneri

Politecnico di Torino

Politecnico di Torino

Guennadi A Kouzaev

Norwegian University of Science and Technology

Hector Pardo Santana

Universidad de Los Lagos, Chile

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Papers by Marco Caruso

Discriminating between indoor and outdoor environments during daily living activities using local magnetic field characteristics and machine learning techniques

Research Square (Research Square), Jan 3, 2024

Wearable technology has rapidly evolved, enabling the integration of various sensors and algorith... more Wearable technology has rapidly evolved, enabling the integration of various sensors and algorithms, opening new possibilities for context-aware applications in fields such as healthcare, fitness tracking, and environmental monitoring. The discrimination between indoor and outdoor environments is crucial. This is often accomplished through technologies like GPS, Wi-Fi, cellular, and Bluetooth. However, these methods have drawbacks, including privacy concerns, high power consumption, and reliance on external infrastructure. To address these challenges, recent studies have explored using the ambient magnetic field for indoor-outdoor detection, though comprehensive validation with real-life data is lacking. This paper seeks to validate specific machine learning techniques -random forest, extreme gradient boosting, and stacked long short-term memory networks -for indoor-outdoor discrimination. The validation exclusively relies on magnetometer data from the daily activities of 20 participants in four cities across three countries (United Kingdom, Germany, and Italy). The study examines factors such as magnetometer placement (feet, lower back, and non-dominant wrist) and preprocessing techniques (features, window size, etc.). Reference data is obtained through a GPS-based algorithm coupled with a geographical database running on a smartphone carried by the participants. The extreme gradient boosting algorithm yielded the best results, with an accuracy of 0.91, an F1-score of 0.90, and an area under the ROC curve of 0.94. These findings confirm the feasibility of extracting precise indoor/outdoor context information from magnetometer data.

An ISB-consistent Denavit-Hartenberg model of the human upper limb for joint kinematics optimization: validation on synthetic and robot data during a typical rehabilitation gesture

2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Jul 11, 2022

On the Detection of High-Quality, High-Density Electromyograms During 80m Sprints: a Case Study

2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Jun 22, 2022

Running speed changes the distribution of excitation within the biceps femoris muscle in 80 m sprints

Scandinavian Journal of Medicine & Science in Sports, Mar 3, 2023

Estimating real-world walking speed from a single wearable device: analytical pipeline, results and lessons learnt from the Mobilise-D technical validation study

Background: Estimation of walking speed from wearable devices requires combining a set of algorit... more Background: Estimation of walking speed from wearable devices requires combining a set of algorithms in a single analytical pipeline. The aim of this study was to validate a pipeline for walking speed estimation and assess its performance across different factors (complexity, speed, and walking bout duration) to make recommendations on the use and validity of wearable devices for real-world mobility analysis. Methods: Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and for 2.5 hours in the real-world, using a wearable device worn on the lower back. Two pipelines for estimating WS were validated across 1298 detected walking bouts, against 1365 walking bouts detected by a multi-sensor reference system. Results: In the laboratory, the mean absolute error (MAE) and mean absolute relative error (MARE) for estimation ...

An ISB-consistent upper limb model based on the Denavit-Hartenberg convention for joint angle estimation during prolonged rehabilitation exercises

Gait & Posture

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

Background: Although digital mobility outcomes (DMOs) can be readily calculated from real-world d... more Background: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices (WD) and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection (GSD), foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. Methods: Twenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture (PFF), 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 hours in the real-world, using a WD worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the WD. We assessed and validated three algorithms for GSD, four for ICD, three for CAD and four for...

Modeling and Kinematic Optimization of the Human Upper Limb for Collaborative Robotics

Mechanisms and machine science, 2022

DoMoMEA: a Home-Based Telerehabilitation System for Stroke Patients

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

After a cerebral stroke, survivors need to follow a neurorehabilitation program including exercis... more After a cerebral stroke, survivors need to follow a neurorehabilitation program including exercises to be executed under a therapist’s supervision or autonomously. Technological solutions are needed to support the early discharge of the patients just after the primary hospital treatments, by still providing an adequate level of rehabilitation. The DoMoMEA Project proposes a fully-wearable m-health solution able to administer a neurorehabilitation therapy in the patient's home or every other place established by the patient for a rehabilitation session. The exploitation of magneto-inertial measurement units only, wirelessly connected to an Android-operated device, provides robustness to different operating conditions and immunity to optical occlusion problems, compared to RGB-D cameras. Patients’ engagement is fostered by the exploitation of the exergame version of the ten rehabilitation exercises, implemented in Unity 3D. Store-and-forward telemonitoring features, supported by cloud-based storage and by a web application accessible from anywhere by medical personnel and patients, enable constant transparent monitoring of the rehabilitation progresses. The clinical trial of the DoMoMEA telerehabilitation system will involve 40 post-stroke patients with mild impairment and will start as soon as the restrictions due to the COVID-19 pandemic will allow to enroll patients.

An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks

Frontiers in Bioengineering and Biotechnology

There is growing interest in the quantification of gait as part of complex motor tasks. This requ... more There is growing interest in the quantification of gait as part of complex motor tasks. This requires gait events (GEs) to be detected under conditions different from straight walking. This study aimed to propose and validate a new marker-based GE detection method, which is also suitable for curvilinear walking and step negotiation. The method was first tested against existing algorithms using data from healthy young adults (YA, n = 20) and then assessed in data from 10 individuals from the following five cohorts: older adults, chronic obstructive pulmonary disease, multiple sclerosis, Parkinson’s disease, and proximal femur fracture. The propagation of the errors associated with GE detection on the calculation of stride length, duration, speed, and stance/swing durations was investigated. All participants performed a variety of motor tasks including curvilinear walking and step negotiation, while reference GEs were identified using a validated methodology exploiting pressure insole...

A comparative accuracy analysis of five sensor fusion algorithms for orientation estimation using magnetic and inertial sensors

Gait & Posture, 2018

Towards an Automatic Parameter Setting for Mimu Sensor Fusion Algorithms

Gait & Posture, 2019

Design and validation of a multi-task, multi-phase protocol for real-world gait simulation

Background: Measuring mobility in daily life entails dealing with confounding factors arising fro... more Background: Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a multi-task and multi-phase protocol for simulating real-world gait accounting for all these factors within a single set of observations carried out within a limited laboratory space, while ensuring minimisation of participant burden and safety.Methods: The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants’ strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohort groups that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson’s Disease (PD), Multiple Sclerosis (MS), Proxim...

Accuracy of a multi-sensor system in stride parameters estimation: comparison of straight and curvilinear portions

Zenodo (CERN European Organization for Nuclear Research), Jan 19, 2022

Orientation Estimation Through Magneto-Inertial Sensor Fusion: A Heuristic Approach for Suboptimal Parameters Tuning

Zenodo (CERN European Organization for Nuclear Research), Sep 21, 2020

Magneto-Inertial Measurement Units (MIMUs) are a valid alternative tool to optical stereophotogra... more Magneto-Inertial Measurement Units (MIMUs) are a valid alternative tool to optical stereophotogrammetry in human motion analysis. The orientation of a MIMU may be estimated by using sensor fusion algorithms. Such algorithms require input parameters that are usually set using a trial-and-error (or grid-search) approach to find the optimal values. However, using trial-and-error requires a known reference orientation, a circumstance rarely occurring in real-life applications. In this paper, we present a way to suboptimally set input parameters, by exploiting the assumption that two MIMUs rigidly connected are expected to show no orientation difference during motion. This approach was validated by applying it to the popular complementary filter by Madgwick et al. and tested on 18 experimental conditions including three commercial products, three angular rates, and two dimensionality motion conditions. Two main findings were observed: i) the selection of the optimal parameter value strongly depends on the specific experimental conditions considered, ii) in 15 out of 18 conditions the errors obtained using the proposed approach and the trial-and-error were coincident, while in the other cases the maximum discrepancy amounted to 2.5 deg and less than 1.5 deg on average.

A wearable multi-sensor system for real world gait analysis

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Nov 1, 2021

Gait analysis is commonly performed in standardized environments, but there is a growing interest... more Gait analysis is commonly performed in standardized environments, but there is a growing interest in assessing gait also in ecological conditions. In this regard, an important limitation is the lack of an accurate mobile gold standard for validating any wearable system, such as continuous monitoring devices mounted on the trunk or wrist. This study therefore deals with the development and validation of a new wearable multi-sensor-based system for digital gait assessment in free-living conditions. In particular, results obtained from five healthy subjects during lab-based and realworld experiments were presented and discussed. The in-lab validation, which assessed the accuracy and reliability of the proposed system, shows median percentage errors smaller than 2% in the estimation of spatio-temporal parameters. The system also proved to be easy to use, comfortable to wear and robust during the out-of-lab acquisitions, showing its feasibility for free-living applications. I. INTRODUCTION Recent literature has shown the relevance of characterising an individual's mobility in real-world conditions for a complete assessment of typical motor abilities [1,2]. This requires the use of activity monitors, e.g. devices including a single inertial measurement unit (IMU), that can be used without causing discomfort thanks to its limited invasivity. In this sense, the most convenient body positionings are trunk and wrist [3]. However, those locations present criticalities for the analysis of gait in terms of reliability, since the farther from the contact point the IMU is placed, the more difficult the estimation of gait-related parameters is. In this respect, the trunk is far from the ground but near to the centre of mass while the wrist is far from both ground and centre of mass. Although the scientific community is actively working on developing and improving algorithms for the above-mentioned solutions, algorithms validation is still performed in the laboratory while capturing simple gait tasks in spatially and temporally limited observation windows [4,5]. Testing single-sensor algorithms outside the laboratory would require a wearable system that is robust and accurate enough to be used as reference in validating other wearable technologies, i.e. a mobile gold standard

Accuracy of the Orientation Estimate Obtained Using Four Sensor Fusion Filters Applied to Recordings of Magneto-Inertial Sensors Moving at Three Rotation Rates

Zenodo (CERN European Organization for Nuclear Research), Jan 25, 2021

Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization

Scientific Data

Wearable devices are used in movement analysis and physical activity research to extract clinical... more Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual’s mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the sci...

Real-time estimation of upper limbs kinematics with IMUs during typical industrial gestures

Procedia Computer Science, 2022

In the context of Industry 4.0, collaborative robotics can be improved by implementing appropriat... more In the context of Industry 4.0, collaborative robotics can be improved by implementing appropriate reactive behavior schemes between human and the robot within the shared workspace. Inertial Measurement Units (IMUs) are widely used for real-time capturing of the human motion in clinical and sports applications, while less attention has been dedicated to IMUs for human-robot interactions in manufacturing industrial field. The aim of the present study was to validate an IMU-based method for real-time estimation of upper limbs kinematics during typical gestures required for industrial assembly tasks. To avoid ferro-magnetic disturbances typical of manufacturing environments, the magnetometers were excluded from the estimate of IMUs orientations. Shoulder and elbow angles were assessed in real-time during pick and place tasks through three IMUs fixed on the upper body of six participants. Results were validated through a stereophotogrammetric motion capture system. Errors associated to the proposed method were moderate and amounted to 2.4 and 3.5 deg on average for shoulder elevation and elbow flexion-extension angles, respectively, confirming their suitability for an industrial context of human-robot collaboration.

A multi-sensor wearable system for the assessment of diseased gait in real-world conditions

Frontiers in Bioengineering and Biotechnology

Introduction: Accurately assessing people’s gait, especially in real-world conditions and in case... more Introduction: Accurately assessing people’s gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors).Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson’s disease, multipl...

Discriminating between indoor and outdoor environments during daily living activities using local magnetic field characteristics and machine learning techniques

Research Square (Research Square), Jan 3, 2024

Wearable technology has rapidly evolved, enabling the integration of various sensors and algorith... more Wearable technology has rapidly evolved, enabling the integration of various sensors and algorithms, opening new possibilities for context-aware applications in fields such as healthcare, fitness tracking, and environmental monitoring. The discrimination between indoor and outdoor environments is crucial. This is often accomplished through technologies like GPS, Wi-Fi, cellular, and Bluetooth. However, these methods have drawbacks, including privacy concerns, high power consumption, and reliance on external infrastructure. To address these challenges, recent studies have explored using the ambient magnetic field for indoor-outdoor detection, though comprehensive validation with real-life data is lacking. This paper seeks to validate specific machine learning techniques -random forest, extreme gradient boosting, and stacked long short-term memory networks -for indoor-outdoor discrimination. The validation exclusively relies on magnetometer data from the daily activities of 20 participants in four cities across three countries (United Kingdom, Germany, and Italy). The study examines factors such as magnetometer placement (feet, lower back, and non-dominant wrist) and preprocessing techniques (features, window size, etc.). Reference data is obtained through a GPS-based algorithm coupled with a geographical database running on a smartphone carried by the participants. The extreme gradient boosting algorithm yielded the best results, with an accuracy of 0.91, an F1-score of 0.90, and an area under the ROC curve of 0.94. These findings confirm the feasibility of extracting precise indoor/outdoor context information from magnetometer data.

An ISB-consistent Denavit-Hartenberg model of the human upper limb for joint kinematics optimization: validation on synthetic and robot data during a typical rehabilitation gesture

2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Jul 11, 2022

On the Detection of High-Quality, High-Density Electromyograms During 80m Sprints: a Case Study

2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Jun 22, 2022

Running speed changes the distribution of excitation within the biceps femoris muscle in 80 m sprints

Scandinavian Journal of Medicine & Science in Sports, Mar 3, 2023

Estimating real-world walking speed from a single wearable device: analytical pipeline, results and lessons learnt from the Mobilise-D technical validation study

Background: Estimation of walking speed from wearable devices requires combining a set of algorit... more Background: Estimation of walking speed from wearable devices requires combining a set of algorithms in a single analytical pipeline. The aim of this study was to validate a pipeline for walking speed estimation and assess its performance across different factors (complexity, speed, and walking bout duration) to make recommendations on the use and validity of wearable devices for real-world mobility analysis. Methods: Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and for 2.5 hours in the real-world, using a wearable device worn on the lower back. Two pipelines for estimating WS were validated across 1298 detected walking bouts, against 1365 walking bouts detected by a multi-sensor reference system. Results: In the laboratory, the mean absolute error (MAE) and mean absolute relative error (MARE) for estimation ...

An ISB-consistent upper limb model based on the Denavit-Hartenberg convention for joint angle estimation during prolonged rehabilitation exercises

Gait & Posture

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

Background: Although digital mobility outcomes (DMOs) can be readily calculated from real-world d... more Background: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices (WD) and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection (GSD), foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. Methods: Twenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture (PFF), 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 hours in the real-world, using a WD worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the WD. We assessed and validated three algorithms for GSD, four for ICD, three for CAD and four for...

Modeling and Kinematic Optimization of the Human Upper Limb for Collaborative Robotics

Mechanisms and machine science, 2022

DoMoMEA: a Home-Based Telerehabilitation System for Stroke Patients

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

After a cerebral stroke, survivors need to follow a neurorehabilitation program including exercis... more After a cerebral stroke, survivors need to follow a neurorehabilitation program including exercises to be executed under a therapist’s supervision or autonomously. Technological solutions are needed to support the early discharge of the patients just after the primary hospital treatments, by still providing an adequate level of rehabilitation. The DoMoMEA Project proposes a fully-wearable m-health solution able to administer a neurorehabilitation therapy in the patient's home or every other place established by the patient for a rehabilitation session. The exploitation of magneto-inertial measurement units only, wirelessly connected to an Android-operated device, provides robustness to different operating conditions and immunity to optical occlusion problems, compared to RGB-D cameras. Patients’ engagement is fostered by the exploitation of the exergame version of the ten rehabilitation exercises, implemented in Unity 3D. Store-and-forward telemonitoring features, supported by cloud-based storage and by a web application accessible from anywhere by medical personnel and patients, enable constant transparent monitoring of the rehabilitation progresses. The clinical trial of the DoMoMEA telerehabilitation system will involve 40 post-stroke patients with mild impairment and will start as soon as the restrictions due to the COVID-19 pandemic will allow to enroll patients.

An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks

Frontiers in Bioengineering and Biotechnology

There is growing interest in the quantification of gait as part of complex motor tasks. This requ... more There is growing interest in the quantification of gait as part of complex motor tasks. This requires gait events (GEs) to be detected under conditions different from straight walking. This study aimed to propose and validate a new marker-based GE detection method, which is also suitable for curvilinear walking and step negotiation. The method was first tested against existing algorithms using data from healthy young adults (YA, n = 20) and then assessed in data from 10 individuals from the following five cohorts: older adults, chronic obstructive pulmonary disease, multiple sclerosis, Parkinson’s disease, and proximal femur fracture. The propagation of the errors associated with GE detection on the calculation of stride length, duration, speed, and stance/swing durations was investigated. All participants performed a variety of motor tasks including curvilinear walking and step negotiation, while reference GEs were identified using a validated methodology exploiting pressure insole...

A comparative accuracy analysis of five sensor fusion algorithms for orientation estimation using magnetic and inertial sensors

Gait & Posture, 2018

Towards an Automatic Parameter Setting for Mimu Sensor Fusion Algorithms

Gait & Posture, 2019

Design and validation of a multi-task, multi-phase protocol for real-world gait simulation

Background: Measuring mobility in daily life entails dealing with confounding factors arising fro... more Background: Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a multi-task and multi-phase protocol for simulating real-world gait accounting for all these factors within a single set of observations carried out within a limited laboratory space, while ensuring minimisation of participant burden and safety.Methods: The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants’ strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohort groups that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson’s Disease (PD), Multiple Sclerosis (MS), Proxim...

Accuracy of a multi-sensor system in stride parameters estimation: comparison of straight and curvilinear portions

Zenodo (CERN European Organization for Nuclear Research), Jan 19, 2022

Orientation Estimation Through Magneto-Inertial Sensor Fusion: A Heuristic Approach for Suboptimal Parameters Tuning

Zenodo (CERN European Organization for Nuclear Research), Sep 21, 2020

Magneto-Inertial Measurement Units (MIMUs) are a valid alternative tool to optical stereophotogra... more Magneto-Inertial Measurement Units (MIMUs) are a valid alternative tool to optical stereophotogrammetry in human motion analysis. The orientation of a MIMU may be estimated by using sensor fusion algorithms. Such algorithms require input parameters that are usually set using a trial-and-error (or grid-search) approach to find the optimal values. However, using trial-and-error requires a known reference orientation, a circumstance rarely occurring in real-life applications. In this paper, we present a way to suboptimally set input parameters, by exploiting the assumption that two MIMUs rigidly connected are expected to show no orientation difference during motion. This approach was validated by applying it to the popular complementary filter by Madgwick et al. and tested on 18 experimental conditions including three commercial products, three angular rates, and two dimensionality motion conditions. Two main findings were observed: i) the selection of the optimal parameter value strongly depends on the specific experimental conditions considered, ii) in 15 out of 18 conditions the errors obtained using the proposed approach and the trial-and-error were coincident, while in the other cases the maximum discrepancy amounted to 2.5 deg and less than 1.5 deg on average.

A wearable multi-sensor system for real world gait analysis

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Nov 1, 2021

Gait analysis is commonly performed in standardized environments, but there is a growing interest... more Gait analysis is commonly performed in standardized environments, but there is a growing interest in assessing gait also in ecological conditions. In this regard, an important limitation is the lack of an accurate mobile gold standard for validating any wearable system, such as continuous monitoring devices mounted on the trunk or wrist. This study therefore deals with the development and validation of a new wearable multi-sensor-based system for digital gait assessment in free-living conditions. In particular, results obtained from five healthy subjects during lab-based and realworld experiments were presented and discussed. The in-lab validation, which assessed the accuracy and reliability of the proposed system, shows median percentage errors smaller than 2% in the estimation of spatio-temporal parameters. The system also proved to be easy to use, comfortable to wear and robust during the out-of-lab acquisitions, showing its feasibility for free-living applications. I. INTRODUCTION Recent literature has shown the relevance of characterising an individual's mobility in real-world conditions for a complete assessment of typical motor abilities [1,2]. This requires the use of activity monitors, e.g. devices including a single inertial measurement unit (IMU), that can be used without causing discomfort thanks to its limited invasivity. In this sense, the most convenient body positionings are trunk and wrist [3]. However, those locations present criticalities for the analysis of gait in terms of reliability, since the farther from the contact point the IMU is placed, the more difficult the estimation of gait-related parameters is. In this respect, the trunk is far from the ground but near to the centre of mass while the wrist is far from both ground and centre of mass. Although the scientific community is actively working on developing and improving algorithms for the above-mentioned solutions, algorithms validation is still performed in the laboratory while capturing simple gait tasks in spatially and temporally limited observation windows [4,5]. Testing single-sensor algorithms outside the laboratory would require a wearable system that is robust and accurate enough to be used as reference in validating other wearable technologies, i.e. a mobile gold standard

Accuracy of the Orientation Estimate Obtained Using Four Sensor Fusion Filters Applied to Recordings of Magneto-Inertial Sensors Moving at Three Rotation Rates

Zenodo (CERN European Organization for Nuclear Research), Jan 25, 2021

Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization

Scientific Data

Wearable devices are used in movement analysis and physical activity research to extract clinical... more Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual’s mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the sci...

Real-time estimation of upper limbs kinematics with IMUs during typical industrial gestures

Procedia Computer Science, 2022

In the context of Industry 4.0, collaborative robotics can be improved by implementing appropriat... more In the context of Industry 4.0, collaborative robotics can be improved by implementing appropriate reactive behavior schemes between human and the robot within the shared workspace. Inertial Measurement Units (IMUs) are widely used for real-time capturing of the human motion in clinical and sports applications, while less attention has been dedicated to IMUs for human-robot interactions in manufacturing industrial field. The aim of the present study was to validate an IMU-based method for real-time estimation of upper limbs kinematics during typical gestures required for industrial assembly tasks. To avoid ferro-magnetic disturbances typical of manufacturing environments, the magnetometers were excluded from the estimate of IMUs orientations. Shoulder and elbow angles were assessed in real-time during pick and place tasks through three IMUs fixed on the upper body of six participants. Results were validated through a stereophotogrammetric motion capture system. Errors associated to the proposed method were moderate and amounted to 2.4 and 3.5 deg on average for shoulder elevation and elbow flexion-extension angles, respectively, confirming their suitability for an industrial context of human-robot collaboration.

A multi-sensor wearable system for the assessment of diseased gait in real-world conditions

Frontiers in Bioengineering and Biotechnology

Introduction: Accurately assessing people’s gait, especially in real-world conditions and in case... more Introduction: Accurately assessing people’s gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors).Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson’s disease, multipl...