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.

Different neurocognitive controls modulate obstacle avoidance through pregnancy.

Understanding why falls during pregnancy occur at over 25% rate over gestation has clinical impacts on the health of pregnant individuals. Attention, proprioception, and perception of the environment are required to prevent trips and falls. This research aimed to understand how the changes to these neurocognitive processes control obstacle avoidance through gestation. Seventeen pregnant participants were tested five times in 6-week intervals. Participants walked an obstacle course (OC), and we analyzed the crossings over obstacles that were set to 10% of participants' body height. Participants also performed an attentional network test (ANT: performance of specific components of attention), an obstacle perception task (OP: ability to visually define an obstacle and translate that to a body posture), and a joint position sense task (JPS: ability to recognize and recreate a joint position from somatosensation). In the OC task, average leading and trailing foot crossing heights significantly reduced by 13% and 23% respectively, with no change in variation, between weeks 13 and 31 of pregnancy, indicating an increased risk of obstacle contact during this time. The variability in minimum leading foot distances from the obstacle was correlated with all three neurocognition tasks (ANT, OP, and JPS). Increased fall rates in the second and third trimesters of pregnancy may be driven by changes in attention, with additional contributions of joint position sense and environmental perception at various stages of gestation. The results imply that a holistic examination on an individual basis may be required to determine individual trip risk and appropriate safety modifications.