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

arXiv: https://arxiv.org/abs/2405.12711 · HTML: https://mlsanity.com/paper/2405.12711/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 21 May 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [A Transformer-based Framework for Multivariate Time Series Representation Learning](https://mlsanity.com/paper/2010.02803/) (2020)
- [MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://mlsanity.com/paper/1903.01945/) (2019)
- [Analyzing the Structure of Attention in a Transformer Language Model](https://mlsanity.com/paper/1906.04284/) (2019)
- [Should You Mask 15% in Masked Language Modeling?](https://mlsanity.com/paper/2202.08005/) (2022)
- [MAEEG: Masked Auto-encoder for EEG Representation Learning](https://mlsanity.com/paper/2211.02625/) (2022)
- [What the [MASK]? Making Sense of Language-Specific BERT Models](https://mlsanity.com/paper/2003.02912/) (2020)
- [Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors](https://mlsanity.com/paper/1708.08989/) (2017)
- [Human Activity Recognition with Convolutional Neural Netowrks](https://mlsanity.com/paper/1906.01935/) (2019)
- [Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning Models](https://mlsanity.com/paper/2310.03512/) (2023)
- [Semi-MAE: Masked Autoencoders for Semi-supervised Vision Transformers](https://mlsanity.com/paper/2301.01431/) (2023)
- [DS-MS-TCN: Otago Exercises Recognition with a Dual-Scale Multi-Stage Temporal Convolutional Network](https://mlsanity.com/paper/2402.02910/) (2024)

## Abstract (arXiv)

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.