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Injury, hospitalization, and even death are common consequences of falling for elderly people. Therefore, early and robust identification of people at risk of recurrent falling is crucial from a preventive point of view. This study aims to evaluate the effectiveness of an interpretable semi-supervised approach in identifying individuals at risk of falls by using the data provided by ankle-mounted IMU sensors. Our method benefits from the cause–effect link between a fall event and balance ability to pinpoint the moments with the highest fall probability. This framework also has the advantage of training on unlabeled data, and one can exploit its interpretation capacities to detect the target while only using patient metadata, especially those in relation to balance characteristics. This study shows that a visual-based self-attention model is able to infer the relationship between a fall event and loss of balance by attributing high values of weight to moments where the vertical acceleration component of the IMU sensors exceeds 5 m/s² during an especially short period. This semi-supervised approach uses interpretable features to highlight the moments of the recording that may explain the score of balance, thus revealing the moments with the highest risk of falling. Our model allows for the detection of 71% of the possible falling risk events in a window of 1 s (500 ms before and after the target) when compared with threshold-based approaches. This type of framework plays a paramount role in reducing the costs of annotation in the case of fall prevention when using wearable devices. Overall, this adaptive tool can provide valuable data to healthcare professionals, and it can assist them in enhancing fall prevention efforts on a larger scale with lower costs.

This paper presents a new platform to monitor the arm and hand activity of stroke patients during rehabilitation exercises in the hospital and at home during their daily living activities. The platform provides relevant data to the therapist in order to assess the patients physical state and adapt the rehabilitation program if necessary. The platform consists of a self-contained smart cup that can be used to perform exercises that are similar to everyday tasks such as drinking. The first smart cup prototype, the design of which was based on interviews regarding the needs of therapists, contains various sensors that collect information about its orientation, the liquid level, its position compared to a reference target and tremors. The prototype also includes audio and visual displays that provide feedback to patients about their movements. Two studies were carried out in conjunction with healthcare professionals and patients. The first study focused on collecting feedback from healthcare professionals to assess the functionalities of the cup and to improve the prototype. Based on this paper, we designed an improved prototype and created a visualization tool for therapists. Finally, we carried out a preliminary study involving nine patients who had experienced an ischemic or hemorrhagic stroke in the previous 24 months. This preliminary study focused on assessing the usability and acceptability of the cup to the patients. The results showed that the cup was very well accepted by eight of the nine patients in monitoring their activity within a rehabilitation center or at home. Moreover, these eight patients had almost no concerns about the design of the cup and its usability.