Stroke represents a global concern that currently affects a significant part of the world's population. Physical rehabilitation plays a fundamental role for stroke patients to recover mobility and improve quality of life. This process is costly, considering that patients must attend face-to-face rehabilitation sessions in hospitals or rehabilitation centers. Plus, there is a lack of specialized medical staff, who are usually insufficient to properly address the growing number of stroke patients that need physical rehabilitation. This situation has been exacerbated by the COVID-19 pandemic, as some of the human resources have been devoted to fight against the pandemic, and the physical presence of rehabilitation patients in hospitals has been severely limited. This paper proposes PhyRe Up!, a novel remote rehabilitation system that uses mixed reality and gamification techniques. PhyRe Up! has been devised for stroke patients to perform therapeutic exercises at home, with great precision, and with the potential supervision of clinicians. The system aims to increase the patient's motivation as well as maintaining the quality of performance for the exercises, similar to the obtained levels when attending face-to-face sessions with therapists. The underlying architecture combines declarative, procedural, and conditional knowledge to manage the rehabilitation process, which offers flexibility and scalability to enhance the capabilities of the proposed system. Experimental results highlight how the combination of mixed reality and gamification significantly influences the accuracy of rehabilitation exercises previously defined by therapists. Particularly, the conducted experiments in the first validation phase of PhyRe Up! shows that our proposal drastically reduces the intermediate steps required to complete an exercise thanks to the provided visual feedback. The accuracy with which the patient performs the assessed exercise for the first time is greater than when using traditional rehabilitation techniques.
Cognitive deficits are very difficult to diagnose during the initial stages; tests typically consist of a patient performing punctual dual-task activities, which are subjectively analyzed to determine the cognitive decline impact on gait. This work supports novel and objective diagnosis methods by stating a baseline on how neurotypical aging affects dual tasks while using a smartphone on the move. With this aim, we propose a twofold research question: Which mobile device tasks performed on the move (dual tasking) have characteristic changes in gait parameters, and which are especially characteristic at older ages? An experiment was conducted with 30 healthy participants where they performed 15 activities (1 single task, 2 traditional dual-tasks and 12 mobile-based dual-tasks) while walking about 50 m. Participants wore a wireless motion tracker (15 sensors) that made the concise analysis of gait possible. The results obtained characterized the gait parameters affected by mobile-based dual-tasking and the impact of normal cognitive decline due to aging. The statistical analysis shows that using smartphone-based dual-tasking produces more significant results than traditional dual-tasking. In the study, 3 out of 10 gait parameters were very significantly affected (p < 0.001) when using the traditional dual tasks, while 5 out of 10 parameters were very significantly affected (p < 0.001) in mobile-based dual-tasking. Moreover, the most characteristic tasks and gait parameters were identified through the obtained results. Future work will focus on applying this knowledge to improve the early diagnosis of MCI.
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