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The higher inter-class separability of information samples in UI-PRMD compared to REHAB24-6 reduces classification complexity, making it extra appropriate for conventional deep-learning fashions. Tools like Technology Acceptance Models (TAMs) (venkatesh2000theoretical, ; venkatesh2008technology, ) capture users’ attitudes, including perceived usefulness and ease of use. We hypothesized that ES would offer high rankings for the system based on expertise acceptance surveys, and we sought to establish key elements that facilitate or hinder adoption and enchancment of methods like ZEST-E.<br><br><br><br> Participants reported that the system was adaptable to individuals, as shown by optimistic PIADS scores (Figure 4) and excessive scores for efficiency and therapeutic efficacy in the ZEQ (Figure 3). Further, ES expressed optimism that a system like ZEST-E would enhance adherence to at-residence exercise. 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