The Otago Exercise Program (OEP) serves as a vital rehabilitation initiative
for older adults, aiming to enhance their strength and balance, and
consequently prevent falls. While Human Activity Recognition (HAR) systems have
been widely employed in recognizing the activities of individuals, existing
systems focus on the duration of macro activities (i.e. a sequence of
repetitions of the same exercise), neglecting the ability to discern micro
activities (i.e. the individual repetitions of the exercises), in the case of
OEP. This study presents a novel semi-supervised machine learning approach
aimed at bridging this gap in recognizing the micro activities of OEP. To
manage the limited dataset size, our model utilizes a Transformer encoder for
feature extraction, subsequently classified by a Temporal Convolutional Network
(TCN). Simultaneously, the Transformer encoder is employed for masked
unsupervised learning to reconstruct input signals. Results indicate that the
masked unsupervised learning task enhances the performance of the supervised
learning (classification task), as evidenced by f1-scores surpassing the
clinically applicable threshold of 0.8. From the micro activities, two
clinically relevant outcomes emerge: counting the number of repetitions of each
exercise and calculating the velocity during chair rising. These outcomes
enable the automatic monitoring of exercise intensity and difficulty in the
daily lives of older adults.

通过将 Transformer 编码器用于特征提取和时序卷积神经网络（TCN）用于分类来识别 Otago 锻炼计划（OEP）中的微操作，并结合掩码无监督学习任务提高有监督学习任务的性能，研究结果显示 f1 - 分数超过临床实用的 0.8 阈值，从而使得可以自动监测老年人日常生活中锻炼的强度和难度。

一种用于 Otago 微标签识别的掩码半监督学习方法

A Masked Semi-Supervised Learning Approach for Otago Micro Labels  Recognition

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。

DS-MS-TCN: 用双尺度多阶段时间卷积网络的奥塔哥锻炼识别

DS-MS-TCN: Otago Exercises Recognition with a Dual-Scale Multi-Stage  Temporal Convolutional Network

Otago Exercise Program (OEP) is a rehabilitation program for older adults to
improve frailty, sarcopenia, and balance. Accurate monitoring of patient
involvement in OEP is challenging, as self-reports (diaries) are often
unreliable. With the development of wearable sensors, Human Activity
Recognition (HAR) systems using wearable sensors have revolutionized
healthcare. However, their usage for OEP still shows limited performance. The
objective of this study is to build an unobtrusive and accurate system to
monitor OEP for older adults. Data was collected from older adults wearing a
single waist-mounted Inertial Measurement Unit (IMU). Two datasets were
collected, one in a laboratory setting, and one at the homes of the patients. A
hierarchical system is proposed with two stages: 1) using a deep learning model
to recognize whether the patients are performing OEP or activities of daily
life (ADLs) using a 10-minute sliding window; 2) based on stage 1, using a
6-second sliding window to recognize the OEP sub-classes performed. The results
showed that in stage 1, OEP could be recognized with window-wise f1-scores over
0.95 and Intersection-over-Union (IoU) f1-scores over 0.85 for both datasets.
In stage 2, for the home scenario, four activities could be recognized with
f1-scores over 0.8: ankle plantarflexors, abdominal muscles, knee bends, and
sit-to-stand. The results showed the potential of monitoring the compliance of
OEP using a single IMU in daily life. Also, some OEP sub-classes are possible
to be recognized for further analysis.

通过使用腰部佩戴的惯性测量单元 (IMU) 进行数据收集，并采用深度学习模型，本研究提出了一个无障碍且准确的系统来监测老年人的 Otago 运动计划。研究结果表明，在实验室和家庭环境中，使用单个 IMU 可以对 Otago 运动计划及其子类进行准确的监测。