The Otago Exercise Program (OEP) represents a crucial rehabilitation
initiative tailored for older adults, aimed at enhancing balance and strength.
Despite previous efforts utilizing wearable sensors for OEP recognition,
existing studies have exhibited limitations in terms of accuracy and
robustness. This study addresses these limitations by employing a single
waist-mounted Inertial Measurement Unit (IMU) to recognize OEP exercises among
community-dwelling older adults in their daily lives. A cohort of 36 older
adults participated in laboratory settings, supplemented by an additional 7
older adults recruited for at-home assessments. The study proposes a Dual-Scale
Multi-Stage Temporal Convolutional Network (DS-MS-TCN) designed for two-level
sequence-to-sequence classification, incorporating them in one loss function.
In the first stage, the model focuses on recognizing each repetition of the
exercises (micro labels). Subsequent stages extend the recognition to encompass
the complete range of exercises (macro labels). The DS-MS-TCN model surpasses
existing state-of-the-art deep learning models, achieving f1-scores exceeding
80% and Intersection over Union (IoU) f1-scores surpassing 60% for all four
exercises evaluated. Notably, the model outperforms the prior study utilizing
the sliding window technique, eliminating the need for post-processing stages
and window size tuning. To our knowledge, we are the first to present a novel
perspective on enhancing Human Activity Recognition (HAR) systems through the
recognition of each repetition of activities.

通过在日常生活中采用单个腰部佩戴惯性测量单元（IMU）来识别社区中老年人进行 Otago 运动项目（OEP）锻炼，使用双尺度多阶段时间卷积网络（DS-MS-TCN）模型实现了基于序列的两级分类，超过现有深度学习模型，在评估的四个锻炼中实现了超过 80％的 F1 分数和超过 60％ 的交集联合（IoU）F1 分数。