Background and aim Regular physical activity preserves functional independence in older adults, yet care-home residents often miss out because personalized supervision is scarce. Autonomous, technology-supported exercise platforms could deliver such guidance without additional staff time—but only if sessions are automatically monitored for safety and quality. We therefore designed a deep learning (DL) system that (a) recognizes individual exercise types and (b) estimates joint angle trajectories from a standard video recording. These outputs are used to compute objective exercise performance metrics (EPMs) such as duration, repetition count, motion variability, and range of motion.

Methods Seven care-home residents (aged between 65–94 years) performed six common rehabilitation exercises in front of a single camera while wearing 17 inertial sensors (Xsens MVN Awinda) that provided ground-truth joint angles. Two-dimensional skeleton poses estimated from the video were fed into a temporal convolutional neural network to recognize the exercises and estimate three-dimensional joint angles. We evaluated exercise segmentation with F1@50 and angle regression with mean per-joint angular error (MPJAE) across nine trunk and lower-limb joints, using leave-one-subject-out cross-validation. Pearson correlations assessed agreement between estimated and ground-truth EPMs.

Results The DL model achieved an F1@50 of 0.92 (± 0.04) for exercise recognition and an MPJAE of 7.7° (± 0.91) for joint angle estimation. The estimated EPMs aligned closely with ground truth, achieving correlation scores of 0.93 (95% CI [0.90, 0.95]) for duration, 0.86 (95% CI [0.80, 0.90]) for repetition count, and between 0.3 and 0.9 for motion variability and range of motion across exercises.

Conclusion The DL algorithm reliably estimates key exercise outcomes from a single video stream. This video-based monitoring pipeline could enable unsupervised, technology-supported exercise assessment in residential care homes while safeguarding session quality and safety. Future work will validate the approach in larger and more diverse cohorts.

Assistive lower-limb exoskeletons (LLEs) have been recognized as promising tools for enhancing physical capacity in stroke survivors. Involving end-users in the early development stages is essential to ensure these technologies meet user needs. Co-design approaches, which actively engage end-users, support this goal. This study aims to (1) evaluate the impact of fatigue on daily living, (2) identify activities that could benefit from LLE assistance, (3) outline design and usability requirements for home-based LLEs, and (4) define physical parameters LLEs should monitor and assess. Discussions were structured using the PERCEPT co-design methodology and thematically analyzed. Four chronic stroke survivors participated in three focus group sessions, each lasting approximately 2 hours. Fatigue was identified as a significant factor in daily life, underscoring the importance of assistive technologies, such as LLEs, to help mitigate exhaustion. Participants recognized LLEs as valuable tools for enhancing physical performance, with benefits for muscle strength, balance, fatigue management, coordination, and general mobility. Design considerations included system modularity, battery efficiency, ease of donning and doffing, and practical needs for daily use. Our findings offer valuable insights into stroke survivors’ design and usability concerns regarding LLEs and provide a foundation for advancing the development and adoption of new assistive technologies.