Enhancing fall risk assessment: instrumenting vision with deep learning during walks.

BACKGROUND: Falls are common in a range of clinical cohorts, where routine risk assessment often comprises subjective visual observation only. Typically, observational assessment involves evaluation of an individual's gait during scripted walking protocols within a lab to identify deficits that potentially increase fall risk, but subtle deficits may not be (readily) observable. Therefore, objective approaches (e.g., inertial measurement units, IMUs) are useful for quantifying high resolution gait characteristics, enabling more informed fall risk assessment by capturing subtle deficits. However, IMU-based gait instrumentation alone is limited, failing to consider participant behaviour and details within the environment (e.g., obstacles). Video-based eye-tracking glasses may provide additional insight to fall risk, clarifying how people traverse environments based on head and eye movements. Recording head and eye movements can provide insights into how the allocation of visual attention to environmental stimuli influences successful navigation around obstacles. Yet, manual review of video data to evaluate head and eye movements is time-consuming and subjective. An automated approach is needed but none currently exists. This paper proposes a deep learning-based object detection algorithm (VARFA) to instrument vision and video data during walks, complementing instrumented gait. METHOD: The approach automatically labels video data captured in a gait lab to assess visual attention and details of the environment. The proposed algorithm uses a YoloV8 model trained on with a novel lab-based dataset. RESULTS: VARFA achieved excellent evaluation metrics (0.93 mAP50), identifying, and localizing static objects (e.g., obstacles in the walking path) with an average accuracy of 93%. Similarly, a U-NET based track/path segmentation model achieved good metrics (IoU 0.82), suggesting that the predicted tracks (i.e., walking paths) align closely with the actual track, with an overlap of 82%. Notably, both models achieved these metrics while processing at real-time speeds, demonstrating efficiency and effectiveness for pragmatic applications. CONCLUSION: The instrumented approach improves the efficiency and accuracy of fall risk assessment by evaluating the visual allocation of attention (i.e., information about when and where a person is attending) during navigation, improving the breadth of instrumentation in this area. Use of VARFA to instrument vision could be used to better inform fall risk assessment by providing behaviour and context data to complement instrumented e.g., IMU data during gait tasks. That may have notable (e.g., personalized) rehabilitation implications across a wide range of clinical cohorts where poor gait and increased fall risk are common.

Validity and reliability of the DANU sports system for walking and running gait assessment.

Objective. Gait assessments have traditionally been analysed in laboratory settings, but this may not reflect natural gait. Wearable technology may offer an alternative due to its versatility. The purpose of the study was to establish the validity and reliability of temporal gait outcomes calculated by the DANU sports system, against a 3D motion capture reference system.Approach. Forty-one healthy adults (26 M, 15 F, age 36.4 ± 11.8 years) completed a series of overground walking and jogging trials and 60 s treadmill walking and running trials at various speeds (8-14 km hr-1), participants returned for a second testing session to repeat the same testing.Main results. For validity, 1406 steps and 613 trials during overground and across all treadmill trials were analysed respectively. Temporal outcomes generated by the DANU sports system included ground contact time, swing time and stride time all demonstrated excellent agreement compared to the laboratory reference (intraclass correlation coefficient (ICC) > 0.900), aside from ground contact time during overground jogging which had good agreement (ICC = 0.778). For reliability, 666 overground and 511 treadmill trials across all speeds were examined. Test re-test agreement was excellent for all outcomes across treadmill trials (ICC > 0.900), except for swing time during treadmill walking which had good agreement (ICC = 0.886). Overground trials demonstrated moderate to good test re-test agreement (ICC = 0.672-0.750), which may be due to inherent variability of self-selected (rather than treadmill set) pacing between sessions.Significance. Overall, this study showed that temporal gait outcomes from the DANU Sports System had good to excellent validity and moderate to excellent reliability in healthy adults compared to an established laboratory reference.

IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring.

Walking/gait quality is a useful clinical tool to assess general health and is now broadly described as the sixth vital sign. This has been mediated by advances in sensing technology, including instrumented walkways and three-dimensional motion capture. However, it is wearable technology innovation that has spawned the highest growth in instrumented gait assessment due to the capabilities for monitoring within and beyond the laboratory. Specifically, instrumented gait assessment with wearable inertial measurement units (IMUs) has provided more readily deployable devices for use in any environment. Contemporary IMU-based gait assessment research has shown evidence of the robust quantifying of important clinical gait outcomes in, e.g., neurological disorders to gather more insightful habitual data in the home and community, given the relatively low cost and portability of IMUs. The aim of this narrative review is to describe the ongoing research regarding the need to move gait assessment out of bespoke settings into habitual environments and to consider the shortcomings and inefficiencies that are common within the field. Accordingly, we broadly explore how the Internet of Things (IoT) could better enable routine gait assessment beyond bespoke settings. As IMU-based wearables and algorithms mature in their corroboration with alternate technologies, such as computer vision, edge computing, and pose estimation, the role of IoT communication will enable new opportunities for remote gait assessment.

Internet-of-Things-Enabled Markerless Running Gait Assessment from a Single Smartphone Camera.

Running gait assessment is essential for the development of technical optimization strategies as well as to inform injury prevention and rehabilitation. Currently, running gait assessment relies on (i) visual assessment, exhibiting subjectivity and limited reliability, or (ii) use of instrumented approaches, which often carry high costs and can be intrusive due to the attachment of equipment to the body. Here, the use of an IoT-enabled markerless computer vision smartphone application based upon Google's pose estimation model BlazePose was evaluated for running gait assessment for use in low-resource settings. That human pose estimation architecture was used to extract contact time, swing time, step time, knee flexion angle, and foot strike location from a large cohort of runners. The gold-standard Vicon 3D motion capture system was used as a reference. The proposed approach performs robustly, demonstrating good (ICC(2,1) > 0.75) to excellent (ICC(2,1) > 0.90) agreement in all running gait outcomes. Additionally, temporal outcomes exhibit low mean error (0.01−0.014 s) in left foot outcomes. However, there are some discrepancies in right foot outcomes, due to occlusion. This study demonstrates that the proposed low-cost and markerless system provides accurate running gait assessment outcomes. The approach may help routine running gait assessment in low-resource environments.

Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data.

Fall risk assessment needs contemporary approaches based on habitual data. Currently, inertial measurement unit (IMU)-based wearables are used to inform free-living spatio-temporal gait characteristics to inform mobility assessment. Typically, a fluctuation of those characteristics will infer an increased fall risk. However, current approaches with IMUs alone remain limited, as there are no contextual data to comprehensively determine if underlying mechanistic (intrinsic) or environmental (extrinsic) factors impact mobility and, therefore, fall risk. Here, a case study is used to explore and discuss how contemporary video-based wearables could be used to supplement arising mobility-based IMU gait data to better inform habitual fall risk assessment. A single stroke survivor was recruited, and he conducted a series of mobility tasks in a lab and beyond while wearing video-based glasses and a single IMU. The latter generated topical gait characteristics that were discussed according to current research practices. Although current IMU-based approaches are beginning to provide habitual data, they remain limited. Given the plethora of extrinsic factors that may influence mobility-based gait, there is a need to corroborate IMUs with video data to comprehensively inform fall risk assessment. Use of artificial intelligence (AI)-based computer vision approaches could drastically aid the processing of video data in a timely and ethical manner. Many off-the-shelf AI tools exist to aid this current need and provide a means to automate contextual analysis to better inform mobility from IMU gait data for an individualized and contemporary approach to habitual fall risk assessment.

Suitability of a Low-Cost Wearable Sensor to Assess Turning in Healthy Adults.

Background: Turning is a complex measure of gait that accounts for over 50% of daily steps. Traditionally, turning has been measured in a research grade laboratory setting, however, there is demand for a low-cost and portable solution to measure turning using wearable technology. This study aimed to determine the suitability of a low-cost inertial sensor-based device (AX6, Axivity) to assess turning, by simultaneously capturing and comparing to a turn algorithm output from a previously validated reference inertial sensor-based device (Opal), in healthy young adults. Methodology: Thirty participants (aged 23.9 ± 4.89 years) completed the following turning protocol wearing the AX6 and reference device: a turn course, a two-minute walk (including 180° turns) and turning in place, alternating 360° turn right and left. Both devices were attached at the lumbar spine, one Opal via a belt, and the AX6 via double sided tape attached directly to the skin. Turning measures included number of turns, average turn duration, angle, velocity, and jerk. Results: Agreement between the outcomes from the AX6 and reference device was good to excellent for all turn characteristics (all ICCs > 0.850) during the turning 360° task. There was good agreement for all turn characteristics (all ICCs > 0.800) during the two-minute walk task, except for moderate agreement for turn angle (ICC 0.683). Agreement for turn outcomes was moderate to good during the turns course (ICCs range; 0.580 to 0.870). Conclusions: A low-cost wearable sensor, AX6, can be a suitable and fit-for-purpose device when used with validated algorithms for assessment of turning outcomes, particularly during continuous turning tasks. Future work needs to determine the suitability and validity of turning in aging and clinical cohorts within low-resource settings.

A proposed computer vision model for running gait assessment.

Running gait assessment is critical in performance optimization and injury prevention. Traditional approaches to running gait assessment are inhibited by unnatural running environments (e.g., indoor lab), varied assessor (i.e., subjective experience) and high costs with traditional reference standard equipment. Thus, development of valid, reproduceable and low-cost approaches are key. Use of wearables such as inertial measurement units have shown promise but despite their flexible use in any environment and reduced cost, they often retain complexities such as connectivity to mobile platforms and stringent attachment protocols. Here, we propose a non-wearable camera-based approach to running gait assessment, focusing on identification of initial contact events within a runner's stride. We investigated different artificial intelligence and object tracking approaches to determine the optimal methodology. A cohort of 40 healthy runners were video recorded (240FPS, multi-angle) during 2-minute running bouts on a treadmill. Validation of the proposed approach is obtained from comparison to manually labelled videos. The computing vision approach can accurately identify initial contact events (ICC(2,1) = 0.902).

Examination of a foot mounted IMU-based methodology for a running gait assessment.

Gait assessment is essential to understand injury prevention mechanisms during running, where high-impact forces can lead to a range of injuries in the lower extremities. Information regarding the running style to increase efficiency and/or selection of the correct running equipment, such as shoe type, can minimize the risk of injury, e.g., matching a runner's gait to a particular set of cushioning technologies found in modern shoes (neutral/support cushioning). Awareness of training or selection of the correct equipment requires an understanding of a runner's biomechanics, such as determining foot orientation when it strikes the ground. Previous work involved a low-cost approach with a foot-mounted inertial measurement unit (IMU) and an associated zero-crossing-based methodology to objectively understand a runner's biomechanics (in any setting) to learn about shoe selection. Here, an investigation of the previously presented ZC-based methodology is presented only to determine general validity for running gait assessment in a range of running abilities from novice (8 km/h) to experienced (16 km/h+). In comparison to Vicon 3D motion tracking data, the presented approach can extract pronation, foot strike location, and ground contact time with good [ICC(2,1) > 0.750] to excellent [ICC(2,1) > 0.900] agreement between 8-12 km/h runs. However, at higher speeds (14 km/h+), the ZC-based approach begins to deteriorate in performance, suggesting that other features and approaches may be more suitable for faster running and sprinting tasks.

Developing and exploring a methodology for multi-modal indoor and outdoor gait assessment.

Gait assessment is emerging as a prominent way to understand impaired mobility and underlying neurological deficits. Various technologies have been used to assess gait inside and outside of laboratory settings, but wearables are the preferred option due to their cost-effective and practical use in both. There are robust conceptual gait models developed to ease the interpretation of gait parameters during indoor and outdoor environments. However, these models examine uni-modal gait characteristics (e.g., spatio-temporal parameters) only. Previous studies reported that understanding the underlying reason for impaired gait requires multi-modal gait assessment. Therefore, this study aims to develop a multi-modal approach using a synchronized inertial and electromyography (EMG) signals. Firstly, initial contact (IC), final contact (FC) moments and corresponding time stamps were identified from inertial data, producing temporal outcomes e.g., step time. Secondly, IC/FC time stamps were used to segment EMG data and define onset and offset times of muscle activities within the gait cycle and its subphases. For investigation purposes, we observed notable differences in temporal characteristics as well as muscle onset/offset timings and amplitudes between indoor and outdoor walking of three stroke survivors. Our preliminary analysis suggests a multi-modal approach may be important to augment and improve current inertial conceptual gait models by providing additional quantitative EMG data.

A feasibility study towards instrumentation of the Sport Concussion Assessment Tool (iSCAT).

The Sports Concussion Assessment Tool (SCAT) is a pen and paper-based evaluation tool for use by healthcare professionals in the acute evaluation of suspected concussion. Here we present a feasibility study towards instrumented SCAT (iSCAT). Traditionally, a healthcare professional subjectively counts errors according to SCAT marking criteria matrix. It is hypothesized that an instrumented version of the test will be more accurate while providing additional digital-based parameters to better inform player management. The feasibility study focuses on the SCAT physical functioning tasks only: double leg stance, single-leg stance, tandem stance and tandem gait. Amateur university rugby players underwent iSCAT testing and data were recorded with 8 inertial units attached at different anatomical locations. Video data were gathered simultaneously as reference. An iSCAT algorithm was used to detect errors and quantify additional concussion-based time and frequency domain parameters to assess participant stability during balance and gait tasks. Future work aims to instrument other SCAT features such as hand-eye coordination while deploying methods within a large concussion project.

Validity of Mobility Lab (version 2) for gait assessment in young adults, older adults and Parkinson's disease.

OBJECTIVE: Gait provides a sensitive measurement for signs of aging and neurodegenerative conditions. Measurement of gait is transitioning from the laboratory environment to the clinic with the use of inertial measurement units, providing a simple and cost-effective assessment tool. However, such assessments first need validation against reference systems. The aim of this study was to validate the APDM Mobility Lab (ML) system (version 2) against a pressure sensor walkway in younger adults (n = 18), older adults (n = 18) and people with mild Parkinson's disease (n = 21) in the laboratory. APPROACH: Participants completed a two-minute walk over a pressure sensor walkway whilst wearing three sensors (strapped to the lumbar spine and both feet). Comparison of output from the systems was then performed. MAIN RESULTS: Overall, we identified that ML provided good to excellent agreement (ICC > 0.75) for gait velocity, stride length, stride length SD, cadence, stride time and stride time SD. Measures of double support time, single support time and swing time had moderate to poor agreement (ICC 0.213-0.725), particularly for younger adults and PD. SIGNIFICANCE: Overall, Mobility Lab provides a valid system for gait data collection for clinical and research application.

Assessment of the ability of open- and closed-loop cueing to improve turning and freezing in people with Parkinson's disease.

Turning impairments are common in Parkinson's disease (PD) and can elicit freezing of gait (FoG). Extensive examination of open-loop cueing interventions has demonstrated that they can ameliorate gait deficits in PD; less is known about efficacy to improve turning. Here, we investigate the immediate effectiveness of open- and closed-loop cueing in improving turning characteristics in people with PD. Twenty-five subjects with and 18 subjects without FoG participated in the study. Subjects turned in place for one minute under single- and dual-task for 3 randomized conditions: (i) Baseline; (ii) Turning to the beat of a metronome (open-loop); and (iii) Turning with phase-dependent tactile biofeedback (closed-loop). Objective measures of freezing, such as % time spent freezing and FoG-ratio, significantly improved when turning with both open-loop and closed-loop cueing compared to baseline. Dual-tasking did not worsen FoG in freezers, but significantly slowed down turns in both groups. Both cueing modalities significantly improved turning smoothness in both groups, but reduced turning velocity and number of turns compared to baseline. Both open and closed-loop cueing markedly improved turning in people with PD. These preliminary observations warrant further exploration of vibrotactile closed-loop cueing to improve mobility in everyday life.