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

A Statistical Approach for Functional Reach-to-Grasp Segmentation Using a Single Inertial Measurement Unit

The aim of this contribution is to present a segmentation method for the identification of voluntary movements from inertial data acquired through a single inertial measurement unit placed on the subject's wrist. Inertial data were recorded from 25 healthy subjects while performing 75 consecutive reach-to-grasp movements. The approach herein presented, called DynAMoS, is based on an adaptive thresholding step on the angular velocity norm, followed by a statistics-based post-processing on the movement duration distribution. Post-processing aims at reducing the number of erroneous transitions in the movement segmentation. We assessed the segmentation quality of this method using a stereophotogrammetric system as the gold standard. Two popular methods already presented in the literature were compared to DynAMoS in terms of the number of movements identified, onset and offset mean absolute errors, and movement duration. Moreover, we analyzed the sub-phase durations of the drinking movement to further characterize the task. The results show that the proposed method performs significantly better than the two state-of-the-art approaches (i.e., percentage of erroneous movements = 3%; onset and offset mean absolute error < 0.08 s), suggesting that DynAMoS could make more effective home monitoring applications for assessing the motion improvements of patients following domicile rehabilitation protocols.

Real-Time Visual Feedback Based on MIMUs Technology Reduces Bowing Errors in Beginner Violin Students

Violin is one of the most complex musical instruments to learn. The learning process requires constant training and many hours of exercise and is primarily based on a student-teacher interaction where the latter guides the beginner through verbal instructions, visual demonstrations, and physical guidance. The teacher's instruction and practice allow the student to learn gradually how to perform the correct gesture autonomously. Unfortunately, these traditional teaching methods require the constant supervision of a teacher and the interpretation of non-real-time feedback provided after the performance. To address these limitations, this work presents a novel interface (Visual Interface for Bowing Evaluation-VIBE) to facilitate student's progression throughout the learning process, even in the absence of direct teacher intervention. The proposed interface allows two key parameters of bowing movements to be monitored, namely, the angle between the bow and the string (i.e., α angle) and the bow tilt (i.e., β angle), providing real-time visual feedback on how to correctly move the bow. Results collected on 24 beginners (12 exposed to visual feedback, 12 in a control group) showed a positive effect of the real-time visual feedback on the improvement of bow control. Moreover, the subjects exposed to visual feedback judged the latter as useful to correct their movement and clear in terms of the presentation of data. Although the task was rated as harder when performed with the additional feedback, the subjects did not perceive the presence of a violin teacher as essential to interpret the feedback.

Development and Evaluation of a Hybrid Measurement System to Determine the Kinematics of the Wrist

Optical Motion Capture Systems (OMCSs) are considered the gold standard for kinematic measurement of human movements. However, in situations such as measuring wrist kinematics during a hairdressing activity, markers can be obscured, resulting in a loss of data. Other measurement methods based on non-optical data can be considered, such as magneto-inertial measurement units (MIMUs). Their accuracy is generally lower than that of an OMCS. In this context, it may be worth considering a hybrid system [MIMU + OMCS] to take advantage of OMCS accuracy while limiting occultation problems. The aim of this work was (1) to propose a methodology for coupling a low-cost MIMU (BNO055) to an OMCS in order to evaluate wrist kinematics, and then (2) to evaluate the accuracy of this hybrid system [MIMU + OMCS] during a simple hairdressing gesture. During hair cutting gestures, the root mean square error compared with the OMCS was 4.53° (1.45°) for flexion/extension, 5.07° (1.30°) for adduction/abduction, and 3.65° (1.19°) for pronation/supination. During combing gestures, they were significantly higher, but remained below 10°. In conclusion, this system allows for maintaining wrist kinematics in case of the loss of hand markers while preserving an acceptable level of precision (<10°) for ergonomic measurement or entertainment purposes.

Investigating concurrent validity of inertial sensors to evaluate multiplanar spine movement

Inertial measurement units (IMUs) offer a portable and inexpensive alternative to traditional optical motion capture systems, and have potential to support clinical diagnosis and treatment of low back pain; however, due to a lack of confidence regarding the validity of IMU-derived metrics, their uptake and acceptance remain a challenge. The objective of this work was to assess the concurrent validity of the Xsens DOT IMUs for tracking multiplanar spine movement, and to evaluate concurrent validity and reliability for estimating clinically relevant metrics relative to gold-standard optical motion capture equipment. Ten healthy controls performed spine range of motion (ROM) tasks, while data were simultaneously tracked from IMUs and optical marker clusters placed over the C7, T12, and S1 vertebrae. Root mean square error (RMSE), mean absolute error (MAE), and intraclass correlation coefficients (ICC2,1) were calculated to assess validity and reliability of absolute (abs; C7, T12, and S1 sensors) and relative joint (rel; intersegmental thoracic, lumbar, and total) motion. Overall RMSEabs = 1.33°, MAEabs = 0.74° ± 0.69, and ICC2,1,abs = 0.953 across all movements, sensors, and planes. Results were slightly better for uniplanar movements when evaluating the primary rotation axis (prim) absolute ROM (MAEabs,prim = 0.56° ± 0.49; ICC2,1,abs,prim = 0.999). Similarly, when evaluating relative intersegmental motion, overall RMSErel = 2.39°, MAErel = 1.10° ± 0.96, and ICC2,1,rel = 0.950, and relative primary rotation axis achieved MAErel,prim = 0.87° ± 0.77, and ICC2,1,rel,prim = 0.994. Findings from this study suggest that these IMUs can be considered valid for tracking multiplanar spine movement, and may be used to objectively assess spine movement and neuromuscular control in clinics.

Gaitmap-An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking

Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and easy-to-use open-source algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. Methods: This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. Conclusion: The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond.

The effect of model complexity on the human center of mass estimation using the statically equivalent serial chain technique

Estimating the human center of mass (CoM) has long been recognized as a highly complex process. A relatively recent and noteworthy technique for CoM estimation that has gained popularity is the statically equivalent serial chain (SESC). This technique employs a remodeling of the human skeleton as a serial chain where the end effector represents the CoM location. In this study, we aimed to evaluate the impact of model complexity on the estimation capability of the SESC technique. To achieve this, we designed and rigorously assessed four distinct models with varying complexities against the static center of pressure (CoP) as reference, by quantifying both the root-mean-square (RMS) and correlation metrics. In addition, the Bland-Altman analysis was utilized to quantify the agreement between the estimations and reference values. The findings revealed that increasing the model complexity significantly improved CoM estimation quality up to a specific threshold. The maximum observed RMS difference among the models reached 9.85 mm. However, the application and task context should be considered, as less complex models still provided satisfactory estimation performance. In conclusion, the evaluation of model complexity demonstrated its impact on CoM estimation using the SESC technique, providing insights into the trade-off between accuracy and complexity in practical applications.

Posturography Approaches: An Insightful Window to Explore the Role of the Brain in Socio-Affective Processes

A significant amount of research has highlighted the importance of a motor component in the brain's processing of emotional, motivational and social information. Posturography has emerged as an interesting way to assess motor correlates associated with this process. In this review, we highlight recent results within the functional context of painful stimulus perception and discuss the interest in broadening the use of posturography to other motivational and societal functional contexts. Although characterized by significant feasibility, the single measurement of the COP's anteroposterior displacement presents limitations for attesting approach-avoidance behavior towards a visual target. Here, we discuss a number of methodological avenues that could go some way towards overcoming these limitations.

A convenient approach for knee kinematics assessment using wearable inertial sensors during home-based rehabilitation: Validation with an optoelectronic system

Rehabilitation services are among the most severely impacted by the COVID-19 pandemic. This has increased the number of people not receiving the needed rehabilitation care. Home-based rehabilitation becomes alternative support to face this greater need. However, monitoring kinematics parameters during rehabilitation exercises is critical for an effective recovery. This work proposes a detailed framework to estimate knee kinematics using a wearable Magnetic and Inertial Measurement Unit (MIMU). That allows at-home monitoring for knee rehabilitation progress. Two MIMU sensors were attached to the shank and thigh segments respectively. First, the absolute orientation of each sensor was estimated using a sensor fusion algorithm. Second, these sensor orientations were transformed to segments orientations using a functional sensor-to-segment (STS) alignment. Third, the relative orientation between segments, i.e., knee joint angle, was computed and the relevant kinematics parameters were extracted. Then, the validity of our approach was evaluated with a gold-standard optoelectronic system. Seven participants completed three to five Timed-Up-and-Go (TUG) tests. The estimated knee angle was compared to the reference angle. Root-mean-square error (RMSE), correlation coefficient, and Bland-Altman analysis were considered as evaluation metrics. Our results showed reasonable accuracy (RMSE < 8°), strong to very-strong correlation (correlation coefficient > 0.86), a mean difference within 1.1°, and agreement limits from -16° to 14°. In addition, no significant difference was found (p-value > 0.05) in extracted kinematics parameters between both systems. The proposed approach might represent a suitable alternative for the assessment of knee rehabilitation progress in a home context.

A Light-Weight Artificial Neural Network for Recognition of Activities of Daily Living

Human activity recognition (HAR) is essential for the development of robots to assist humans in daily activities. HAR is required to be accurate, fast and suitable for low-cost wearable devices to ensure portable and safe assistance. Current computational methods can achieve accurate recognition results but tend to be computationally expensive, making them unsuitable for the development of wearable robots in terms of speed and processing power. This paper proposes a light-weight architecture for recognition of activities using five inertial measurement units and four goniometers attached to the lower limb. First, a systematic extraction of time-domain features from wearable sensor data is performed. Second, a small high-speed artificial neural network and line search method for cost function optimization are used for activity recognition. The proposed method is systematically validated using a large dataset composed of wearable sensor data from seven activities (sitting, standing, walking, stair ascent/descent, ramp ascent/descent) associated with eight healthy subjects. The accuracy and speed results are compared against methods commonly used for activity recognition including deep neural networks, convolutional neural networks, long short-term memory and convolutional-long short-term memory hybrid networks. The experiments demonstrate that the light-weight architecture can achieve a high recognition accuracy of 98.60%, 93.10% and 84.77% for seen data from seen subjects, unseen data from seen subjects and unseen data from unseen subjects, respectively, and an inference time of 85 μs. The results show that the proposed approach can perform accurate and fast activity recognition with a reduced computational complexity suitable for the development of portable assistive devices.

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

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, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity. Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72-4.87 steps/min, stride length 0.04-0.06 m, walking speed 0.03-0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.

Base of Support, Step Length and Stride Width Estimation during Walking Using an Inertial and Infrared Wearable System

The analysis of the stability of human gait may be effectively performed when estimates of the base of support are available. The base of support area is defined by the relative position of the feet when they are in contact with the ground and it is closely related to additional parameters such as step length and stride width. These parameters may be determined in the laboratory using either a stereophotogrammetric system or an instrumented mat. Unfortunately, their estimation in the real world is still an unaccomplished goal. This study aims at proposing a novel, compact wearable system, including a magneto-inertial measurement unit and two time-of-flight proximity sensors, suitable for the estimation of the base of support parameters. The wearable system was tested and validated on thirteen healthy adults walking at three self-selected speeds (slow, comfortable, and fast). Results were compared with the concurrent stereophotogrammetric data, used as the gold standard. The root mean square errors for the step length, stride width and base of support area varied from slow to high speed between 10-46 mm, 14-18 mm, and 39-52 cm², respectively. The mean overlap of the base of support area as obtained with the wearable system and with the stereophotogrammetric system ranged between 70% and 89%. Thus, this study suggested that the proposed wearable solution is a valid tool for the estimation of the base of support parameters out of the laboratory.

Impact of Gait Events Identification through Wearable Inertial Sensors on Clinical Gait Analysis of Children with Idiopathic Toe Walking

Idiopathic toe walking (ITW) is a gait deviation characterized by forefoot contact with the ground and excessive ankle plantarflexion over the entire gait cycle observed in otherwise-typical developing children. The clinical evaluation of ITW is usually performed using optoelectronic systems analyzing the sagittal component of ankle kinematics and kinetics. However, in standardized laboratory contexts, these children can adopt a typical walking pattern instead of a toe walk, thus hindering the laboratory-based clinical evaluation. With these premises, measuring gait in a more ecological environment may be crucial in this population. As a first step towards adopting wearable clinical protocols embedding magneto-inertial sensors and pressure insoles, this study analyzed the performance of three algorithms for gait events identification based on shank and/or foot sensors. Foot strike and foot off were estimated from gait measurements taken from children with ITW walking barefoot and while wearing a foot orthosis. Although no single algorithm stands out as best from all perspectives, preferable algorithms were devised for event identification, temporal parameters estimate and heel and forefoot rocker identification, depending on the barefoot/shoed condition. Errors more often led to an erroneous characterization of the heel rocker, especially in shoed condition. The ITW gait specificity may cause errors in the identification of the foot strike which, in turn, influences the characterization of the heel rocker and, therefore, of the pathologic ITW behavior.

A Narrative Review on Wearable Inertial Sensors for Human Motion Tracking in Industrial Scenarios

Industry 4.0 has promoted the concept of automation, supporting workers with robots while maintaining their central role in the factory. To guarantee the safety of operators and improve the effectiveness of the human-robot interaction, it is important to detect the movements of the workers. Wearable inertial sensors represent a suitable technology to pursue this goal because of their portability, low cost, and minimal invasiveness. The aim of this narrative review was to analyze the state-of-the-art literature exploiting inertial sensors to track the human motion in different industrial scenarios. The Scopus database was queried, and 54 articles were selected. Some important aspects were identified: (i) number of publications per year; (ii) aim of the studies; (iii) body district involved in the motion tracking; (iv) number of adopted inertial sensors; (v) presence/absence of a technology combined to the inertial sensors; (vi) a real-time analysis; (vii) the inclusion/exclusion of the magnetometer in the sensor fusion process. Moreover, an analysis and a discussion of these aspects was also developed.

Synthesising 2D Video from 3D Motion Data for Machine Learning Applications

To increase the utility of legacy, gold-standard, three-dimensional (3D) motion capture datasets for computer vision-based machine learning applications, this study proposed and validated a method to synthesise two-dimensional (2D) video image frames from historic 3D motion data. We applied the video-based human pose estimation model OpenPose to real (in situ) and synthesised 2D videos and compared anatomical landmark keypoint outputs, with trivial observed differences (2.11−3.49 mm). We further demonstrated the utility of the method in a downstream machine learning use-case in which we trained and then tested the validity of an artificial neural network (ANN) to estimate ground reaction forces (GRFs) using synthesised and real 2D videos. Training an ANN to estimate GRFs using eight OpenPose keypoints derived from synthesised 2D videos resulted in accurate waveform GRF estimations (r > 0.9; nRMSE < 14%). When compared with using the smaller number of real videos only, accuracy was improved by adding the synthetic views and enlarging the dataset. The results highlight the utility of the developed approach to enlarge small 2D video datasets, or to create 2D video images to accompany 3D motion capture datasets to make them accessible for machine learning applications.

A Novel Wireless Low-Cost Inclinometer Made from Combining the Measurements of Multiple MEMS Gyroscopes and Accelerometers

Structural damage detection using inclinometers is getting wide attention from researchers. However, the high price of inclinometers limits this system to unique structures with a relatively high structural health monitoring (SHM) budget. This paper presents a novel low-cost inclinometer, the low-cost adaptable reliable angle-meter (LARA), which combines five gyroscopes and five accelerometers to measure inclination. LARA incorporates Internet of Things (IoT)-based microcontroller technology enabling wireless data streaming and free commercial software for data acquisition. This paper investigates the accuracy, resolution, Allan variance and standard deviation of LARA produced with a different number of combined circuits, including an accelerometer and a gyroscope. To validate the accuracy and resolution of the developed device, its results are compared with those obtained by numerical slope calculations and a commercial inclinometer (HI-INC) in laboratory conditions. The results of a load test experiment on a simple beam model show the high accuracy of LARA (0.003 degrees). The affordability and high accuracy of LARA make it applicable for structural damage detection on bridges using inclinometers.

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

Several biomedical contexts such as diagnosis, rehabilitation, and ergonomics require an accurate estimate of human upper limbs kinematics. Wearable inertial measurement units (IMU s) represent a suitable solution because of their unobtrusiveness, portability, and low-cost. However, the time-integration of the gyroscope angular velocity leads to an unbounded orientation drift affecting both angular and linear displacements over long observation interval. In this work, a Denavit-Hartenberg model of the upper limb was defined in accordance with the guidelines of the International Society of Biomechanics and exploited to design an optimization kinematics process. This procedure estimated the joint angles by minimizing the difference between the modelled and IMU-driven orientation of upper arm and forearm. In addition, reasonable constraints were added to limit the drift influence on the final joint kinematics accuracy. The validity of the procedure was tested on synthetic and experimental data acquired with a robotic arm over 20 minutes. Average rms errors amounted to 2.8 deg and 1.1 for synthetic and robot data, respectively. Clinical Relevance - The proposed method has the potential to improve robustness and accuracy of multi-joint kinematics estimation in the general contexts of home-based tele-rehabilitation interventions. In this respect adoption of multi-segmental kinematic model along with physiological joint constraints could contribute to address current limitations associated to unsupervised analysis in terms of monitoring and outcome assessment.

A functional calibration protocol for ankle plantar-dorsiflexion estimate using magnetic and inertial measurement units: Repeatability and reliability assessment

The ankle joint complex presents a tangled functional anatomy, which understanding is fundamental to effectively estimate its kinematics on the sagittal plane. Protocols based on the use of magnetic and inertial measurement units (MIMUs) currently do not take in due account this factor. To this aim, a joint coordinate system for the ankle joint complex is proposed, along with a protocol to perform its anatomical calibration using MIMUs, consisting in a combination of anatomical functional calibrations of the tibiotalar axis and static acquisitions. Protocol repeatability and reliability were tested according to the metrics proposed in Schwartz et al. (2004) involving three different operators performing the protocol three times on ten participants, undergoing instrumented gait analysis through both stereophotogrammetry and MIMUs. Instrumental reliability was evaluated comparing the MIMU-derived kinematic traces with the stereophotogrammetric ones, obtained with the same protocol, through the linear fit method. A total of 270 gait cycles were considered. Results showed that the protocol was repeatable and reliable for what concerned the operators (0.4 ± 0.4 deg and 0.8 ± 0.5 deg, respectively). Instrumental reliability analysis showed a mean RMSD of 3.0 ± 1.3 deg, a mean offset of 9.4 ± 8.4 deg and a mean linear relationship strength of R² = 0.88 ± 0.08. With due caution, the protocol can be considered both repeatable and reliable. Further studies should pay attention to the other ankle degrees of freedom as well as on the angular convention to compute them.

Detection of balance disorders using rotations around vertical axis and an artificial neural network

Vestibular impairments affect patients' movements and can result in difficulties with daily life activities. The main aim of this study is to answer the question whether a simple and short test such as rotation about a vertical axis can be an objective method of assessing balance dysfunction in patients with unilateral vestibular impairments. A 360˚ rotation test was performed using six MediPost devices. The analysis was performed in three ways: (1) the analytical approach based only on data from one sensor; (2) the analytical approach based on data from six sensors; (3) the artificial neural network (ANN) approach based on data from six sensors. For approaches 1 and 2 best results were obtained using maximum angular velocities (MAV) of rotation and rotation duration (RD), while approach 3 used 11 different features. The following sensitivities and specificities were achieved: for approach 1: MAV—80% and 60%, RD—69% and 74%; for approach 2: 61% and 85% and RD—74% and 56%; for approach 3: 88% and 84%. The ANN-based six-sensor approach revealed the best sensitivity and specificity among parameters studied, however one-sensor approach might be a simple screening test used e.g. for rehabilitation purposes.

Test-Retest Reliability of Kinematic and Temporal Outcome Measures for Clinical Gait and Stair Walking Tests, Based on Wearable Inertial Sensors

It is important to assess gait function in neurological disorders. A common outcome measure from clinical walking tests is average speed, which is reliable but does not capture important kinematical and temporal aspects of gait function. An extended gait analysis must be time efficient and reliable to be included in the clinical routine. The aim of this study was to add an inertial sensor system to a gait test battery and analyze the test-retest reliability of kinematic and temporal outcome measures. Measurements and analyses were performed in the hospital environment by physiotherapists using customized software. In total, 22 healthy persons performed comfortable gait, fast gait, and stair walking, with 12 inertial sensors attached to the feet, shank, thigh, pelvis, thorax, and arms. Each person participated in 2 test sessions, with about 3-6 days between the sessions. Kinematics were calculated based on a sensor fusion algorithm. Sagittal peak angles, sagittal range of motion, and stride frequency were derived. Intraclass-correlation coefficients were determined to analyze the test-retest reliability, which was good to excellent for comfortable and fast gait, with exceptions for hip, knee, and ankle peak angles during fast gait, which showed moderate reliability, and fast gait stride frequency, which showed poor reliability. In stair walking, all outcome measures except shoulder extension showed good to excellent reliability. Inertial sensors have the potential to improve the clinical evaluation of gait function in neurological patients, but this must be verified in patient groups.

An Open-Source and Wearable System for Measuring 3D Human Motion in Real-Time

OBJECTIVE

Analyzing human motion is essential for diagnosing movement disorders and guiding rehabilitation for conditions like osteoarthritis, stroke, and Parkinson's disease. Optical motion capture systems are the standard for estimating kinematics, but the equipment is expensive and requires a predefined space. While wearable sensor systems can estimate kinematics in any environment, existing systems are generally less accurate than optical motion capture. Many wearable sensor systems require a computer in close proximity and use proprietary software, limiting experimental reproducibility.

METHODS

Here, we present OpenSenseRT, an open-source and wearable system that estimates upper and lower extremity kinematics in real time by using inertial measurement units and a portable microcontroller.

RESULTS

We compared the OpenSenseRT system to optical motion capture and found an average RMSE of 4.4 degrees across 5 lower-limb joint angles during three minutes of walking and an average RMSE of 5.6 degrees across 8 upper extremity joint angles during a Fugl-Meyer task. The open-source software and hardware are scalable, tracking 1 to 14 body segments, with one sensor per segment. A musculoskeletal model and inverse kinematics solver estimate Kinematics in real-time. The computation frequency depends on the number of tracked segments, but is sufficient for real-time measurement for many tasks of interest; for example, the system can track 7 segments at 30 Hz in real-time. The system uses off-the-shelf parts costing approximately $100 USD plus $20 for each tracked segment.

SIGNIFICANCE

The OpenSenseRT system is validated against optical motion capture, low-cost, and simple to replicate, enabling movement analysis in clinics, homes, and free-living settings.

Predicting Axial Impairment in Parkinson's Disease through a Single Inertial Sensor

Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson’s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. Methods: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. Results: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). Conclusions: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.

Technical validation of real-world monitoring of gait: a multicentric observational study

INTRODUCTION

Existing mobility endpoints based on functional performance, physical assessments and patient self-reporting are often affected by lack of sensitivity, limiting their utility in clinical practice. Wearable devices including inertial measurement units (IMUs) can overcome these limitations by quantifying digital mobility outcomes (DMOs) both during supervised structured assessments and in real-world conditions. The validity of IMU-based methods in the real-world, however, is still limited in patient populations. Rigorous validation procedures should cover the device metrological verification, the validation of the algorithms for the DMOs computation specifically for the population of interest and in daily life situations, and the users' perspective on the device.

METHODS AND ANALYSIS

This protocol was designed to establish the technical validity and patient acceptability of the approach used to quantify digital mobility in the real world by Mobilise-D, a consortium funded by the European Union (EU) as part of the Innovative Medicine Initiative, aiming at fostering regulatory approval and clinical adoption of DMOs.After defining the procedures for the metrological verification of an IMU-based device, the experimental procedures for the validation of algorithms used to calculate the DMOs are presented. These include laboratory and real-world assessment in 120 participants from five groups: healthy older adults; chronic obstructive pulmonary disease, Parkinson's disease, multiple sclerosis, proximal femoral fracture and congestive heart failure. DMOs extracted from the monitoring device will be compared with those from different reference systems, chosen according to the contexts of observation. Questionnaires and interviews will evaluate the users' perspective on the deployed technology and relevance of the mobility assessment.

ETHICS AND DISSEMINATION

The study has been granted ethics approval by the centre's committees (London-Bloomsbury Research Ethics committee; Helsinki Committee, Tel Aviv Sourasky Medical Centre; Medical Faculties of The University of Tübingen and of the University of Kiel). Data and algorithms will be made publicly available.

TRIAL REGISTRATION NUMBER

ISRCTN (12246987).

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

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.

RIANN—A Robust Neural Network Outperforms Attitude Estimation Filters

Inertial-sensor-based attitude estimation is a crucial technology in various applications, from human motion tracking to autonomous aerial and ground vehicles. Application scenarios differ in characteristics of the performed motion, presence of disturbances, and environmental conditions. Since state-of-the-art attitude estimators do not generalize well over these characteristics, their parameters must be tuned for the individual motion characteristics and circumstances. We propose RIANN, a ready-to-use, neural network-based, parameter-free, real-time-capable inertial attitude estimator, which generalizes well across different motion dynamics, environments, and sampling rates, without the need for application-specific adaptations. We gather six publicly available datasets of which we exploit two datasets for the method development and the training, and we use four datasets for evaluation of the trained estimator in three different test scenarios with varying practical relevance. Results show that RIANN outperforms state-of-the-art attitude estimation filters in the sense that it generalizes much better across a variety of motions and conditions in different applications, with different sensor hardware and different sampling frequencies. This is true even if the filters are tuned on each individual test dataset, whereas RIANN was trained on completely separate data and has never seen any of these test datasets. RIANN can be applied directly without adaptations or training and is therefore expected to enable plug-and-play solutions in numerous applications, especially when accuracy is crucial but no ground-truth data is available for tuning or when motion and disturbance characteristics are uncertain. We made RIANN publicly available.

A Comparison of Three Neural Network Approaches for Estimating Joint Angles and Moments from Inertial Measurement Units

The application of artificial intelligence techniques to wearable sensor data may facilitate accurate analysis outside of controlled laboratory settings—the holy grail for gait clinicians and sports scientists looking to bridge the lab to field divide. Using these techniques, parameters that are difficult to directly measure in-the-wild, may be predicted using surrogate lower resolution inputs. One example is the prediction of joint kinematics and kinetics based on inputs from inertial measurement unit (IMU) sensors. Despite increased research, there is a paucity of information examining the most suitable artificial neural network (ANN) for predicting gait kinematics and kinetics from IMUs. This paper compares the performance of three commonly employed ANNs used to predict gait kinematics and kinetics: multilayer perceptron (MLP); long short-term memory (LSTM); and convolutional neural networks (CNN). Overall high correlations between ground truth and predicted kinematic and kinetic data were found across all investigated ANNs. However, the optimal ANN should be based on the prediction task and the intended use-case application. For the prediction of joint angles, CNNs appear favourable, however these ANNs do not show an advantage over an MLP network for the prediction of joint moments. If real-time joint angle and joint moment prediction is desirable an LSTM network should be utilised.

BROAD—A Benchmark for Robust Inertial Orientation Estimation

Inertial measurement units (IMUs) enable orientation, velocity, and position estimation in several application domains ranging from robotics and autonomous vehicles to human motion capture and rehabilitation engineering. Errors in orientation estimation greatly affect any of those motion parameters. The present work explains the main challenges in inertial orientation estimation (IOE) and presents an extensive benchmark dataset that includes 3D inertial and magnetic data with synchronized optical marker-based ground truth measurements, the Berlin Robust Orientation Estimation Assessment Dataset (BROAD). The BROAD dataset consists of 39 trials that are conducted at different speeds and include various types of movement. Thereof, 23 trials are performed in an undisturbed indoor environment, and 16 trials are recorded with deliberate magnetometer and accelerometer disturbances. We furthermore propose error metrics that allow for IOE accuracy evaluation while separating the heading and inclination portions of the error and introduce well-defined benchmark metrics. Based on the proposed benchmark, we perform an exemplary case study on two widely used openly available IOE algorithms. Due to the broad range of motion and disturbance scenarios, the proposed benchmark is expected to provide valuable insight and useful tools for the assessment, selection, and further development of inertial sensor fusion methods and IMU-based application systems.