Physical Activity Comparison Between Body Sides in Hemiparetic Patients Using Wearable Motion Sensors in Free-Living and Therapy: A Case Series

Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation

We propose a co-simulation framework comprising biomechanical human body models and wearable inertial sensor models to analyse gait events dynamically, depending on inertial sensor type, sensor positioning, and processing algorithms. A total of 960 inertial sensors were virtually attached to the lower extremities of a validated biomechanical model and shoe model. Walking of hemiparetic patients was simulated using motion capture data (kinematic simulation). Accelerations and angular velocities were synthesised according to the inertial sensor models. A comprehensive error analysis of detected gait events versus reference gait events of each simulated sensor position across all segments was performed. For gait event detection, we considered 1-, 2-, and 4-phase gait models. Results of hemiparetic patients showed superior gait event estimation performance for a sensor fusion of angular velocity and acceleration data with lower nMAEs (9%) across all sensor positions compared to error estimation with acceleration data only. Depending on algorithm choice and parameterisation, gait event detection performance increased up to 65%. Our results suggest that user personalisation of IMU placement should be pursued as a first priority for gait phase detection, while sensor position variation may be a secondary adaptation target. When comparing rotatory and translatory error components per body segment, larger interquartile ranges of rotatory errors were observed for all phase models i.e., repositioning the sensor around the body segment axis was more harmful than along the limb axis for gait phase detection. The proposed co-simulation framework is suitable for evaluating different sensor modalities, as well as gait event detection algorithms for different gait phase models. The results of our analysis open a new path for utilising biomechanical human digital twins in wearable system design and performance estimation before physical device prototypes are deployed.

Wearable devices for tracking physical activity in the community after an acquired brain injury: A systematic review

OBJECTIVE

The application of wearable devices in individuals with acquired brain injury (ABI) resulting from stroke or traumatic brain injury (TBI) for monitoring physical activity (PA) has been relatively recent. The current systematic review aims to provide insights into the adaption of these devices, outcome metrics, and their transition from the laboratory to the community for PA monitoring of individuals with ABI.

LITERATURE SURVEY

The PubMed and Google Scholar databases were systematically reviewed using appropriate search terms. A total of 20 articles were reviewed from the past 15 years.

METHODOLOGY

Articles were classified into three categories - PA measurement studies, PA classification studies, and validation studies. The quality of studies was assessed using a quality appraisal checklist.

SYNTHESIS

It was found that the transition of wearable devices from in-lab to community-based studies in individuals with stroke has started but is not widespread. The transition of wearable devices in the community has not yet started for individuals with TBI. Accelerometer-based devices were more frequently chosen than pedometers and inertial measurement units. No consensus on a preferred wearable device (make or model) or wear location could be identified, though step count was the most common outcome metric. The accuracy and validity of most outcome metrics used in the community were not reported for many studies.

CONCLUSIONS

To facilitate future studies use wearable devices for PA measurement in the community, we recommend that researchers provide details on the accuracy and validity of the outcome metrics specific to the study environment. Once the accuracy and validity are established for a specific population, wearable devices and their derived outcomes can provide objective information on mobility impairment as well as the effect of rehabilitation in the community. This article is protected by copyright. All rights reserved.

The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review

This article presents an overview of fifty-eight articles dedicated to the evaluation of physical activity in free-living conditions using wearable motion sensors. This review provides a comprehensive summary of the technical aspects linked to sensors (types, number, body positions, and technical characteristics) as well as a deep discussion on the protocols implemented in free-living conditions (environment, duration, instructions, activities, and annotation). Finally, it presents a description and a comparison of the main algorithms and processing tools used for assessing physical activity from raw signals.

Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis

We present a fundamentally new approach to design and assess wearable motion systems based on biomechanical simulation and sensor data synthesis. We devise a methodology of personal biomechanical models and virtually attach sensor models to body parts, including sensor positions frequently considered for wearable devices. The simulation enables us to synthesise motion sensor data, which is subsequently considered as input for gait marker estimation algorithms. We evaluated our methodology in two case studies, including running athletes and hemiparetic patients. Our analysis shows that running speed affects gait marker estimation performance. Estimation error of stride duration varies between athletes across 834 simulated sensor positions and can soar up to 54%, i.e. 404 ms. In walking patients after stroke, we show that gait marker performance differs between affected and less-affected body sides and optimal sensor positions change over a period of movement therapy intervention. For both case studies, we observe that optimal gait marker estimation performance benefits from personally selected sensor positions and robust algorithms. Our methodology enables wearable designers and algorithm developers to rapidly analyse the design options and create personalised systems where needed, e.g. for patients with movement disorders.