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All Journal International Journal of Artificial Intelligence Research
Nguyen, Tien Manh
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Computer Science & IT Electrical & Electronics Engineering

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Using a Semi-supervised Learning Model for Recognition of Human Daily Activities from Wearable Sensor Data Nguyen, Tien Manh; Motoki, Takagi
International Journal of Artificial Intelligence Research Vol 8, No 1 (2024): June 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1146

Abstract

The application of Machine Learning (ML) and Artificial Intelligence (AI) is growing, and also becoming more important as the aging population increases. Smart support systems for distinguish Activities of Daily Living (ADL) can help the elders live more independently and safely. Many machine learning methods have been proposed for Human Activity Recognition (HAR), including complex networks containing convolutional, recurrent, and attentional layers. This study explores the application of ML techniques in ADL classification, leveraging wearable devices' time-series data capturing various parameters such as acceleration. The acceleration data obtained from sensors is so huge that it is difficult and expensive to accurately label every sample collected, so this study applies the Semi-supervised Learning model to unlabeled samples. Long Short-Term Memory (LSTM) has always been used for time series data such as acceleration, and recently, the Transformer model has emerged in many applications such as Natural Language Processing (NLP) or creating ChatGPT. In this study we proposed ADL classification method using the Self-Attention Transformer block and the Recurrent LSTM block and evaluated their results. After comparison, the model built with LSTM block gives better results than the model built with Transformer block.
Research on the Application of Artificial Intelligence in Hand Rehabilitation by Estimating Hand Grip Force using EMG Data Nguyen, Tien Manh; Takagi, Motoki; Nguyen, Trung Thanh; Tran, Hieu Huy; Dao, Khanh Viet Trong
International Journal of Artificial Intelligence Research Vol 9, No 1 (2025): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1381

Abstract

The human hand is a complex and functionally significant anatomical structure, playing a critical role in daily activities, communication, and professional tasks. Any impairment due to injury, neurological disorders, or musculoskeletal diseases can severely affect an individual's quality of life. Conditions such as stroke-induced hemiparesis, arthritis, carpal tunnel syndrome, and tendon injuries often necessitate rehabilitation to restore function, minimize pain, and prevent secondary complications. Traditional rehabilitation approaches, while beneficial, generally follow a standardized methodology, failing to account for individual variations in muscle strength, neuroplasticity, and adaptive capacity.Modern rehabilitation methods leverage advanced technologies such as electromyography (EMG) and hand grip force measurement to enhance therapy effectiveness. Additionally, artificial intelligence (AI) applications, particularly Long Short-Term Memory (LSTM) networks and Transformer models, have emerged as promising tools for personalized rehabilitation. These models analyze EMG signals to predict hand movement intentions, enabling adaptive rehabilitation strategies tailored to individual needs.  This study focuses on the construction of a real-time EMG signal acquisition system and uses them as input to LSTM and Transformer models to compare and analyze the performance of the two types of models. By demonstrating the superiority of applying AI for personalization over the general AI approach, this study highlights the potential of AI in hand rehabilitation in particular and healthcare in general with its ability to specialize for each individual patient.
Co-Authors Dao, Khanh Viet Trong Motoki, Takagi Nguyen, Trung Thanh Takagi, Motoki Tran, Hieu Huy