Using video technology and AI within Parkinson’s disease free-living fall risk assessment

Falls are a major concern for people with Parkinson's disease (PwPD), but accurately assessing real world falls risk beyond the clinic is challenging. Contemporary technologies could enable the capture of objective and high-resolution data to better inform falls risk through measurement of everyday factors (e.g., obstacles) that contribute to falls. Wearable inertial measurement units (IMUs) capture objective high-resolution walking/gait data in all environments but are limited by not providing absolute clarity on contextual information (i.e., obstacles) that could greatly influence how gait is interpreted. Video-based data could compliment IMU-based data for a comprehensive free-living falls risk assessment. The objective of this study was twofold. First, pilot work was conducted to propose a novel artificial intelligence (AI) algorithm for use with wearable video-based eye-tracking glasses to compliment IMU gait data to better inform free-living falls risk in PwPD. The suggested approach (based on a fine-tuned You Only Look Once version 8 (YOLOv8) object detection algorithm) can accurately detect and contextualize objects (mAP50 = 0.81) in the environment while also providing insights into where the PwPD is looking, which could better inform falls risk. Second, we investigated the perceptions of PwPD via a focus group discussion regarding the adoption of video technologies and AI during their everyday lives to better inform their own falls risk. This second aspect of the study is important as traditionally there may be clinical and patient apprehension due to ethical and privacy concerns on the use of wearable cameras to capture real-world video. Thematic content analysis was used to analyse transcripts and develop core themes and categories. Here, PwPD agreed on ergonomically designed wearable video-based glasses as an optimal mode of video data capture, ensuring discreteness, and negating any public stigma on use of research style equipment. PwPD also emphasized the need for control in AI-assisted data processing to uphold privacy which could overcome concerns on the adoption of video to better inform IMU-based gait and free-living falls risk. Contemporary technologies (wearable video glasses and AI) can provide a holistic approach to falls risk, that PwPD recognise as helpful and safe to use.