Scholarlink

<p>This work proposes a system that uses wireless sensor nodes and surface electromyography (sEMG) data to monitor and recognize human activities in bed. The contributions of this work are that we implement 2.4&#xa0;GHz IEEE 802.15.4 wireless sensor nodes combined with a human body-attached sEMG sensor, where sEMG data are wirelessly sent to a receiver connected to a computer that serves as the processing center. Human activities in bed, including rapid breathing, seizure sleeping, falling from the bed, and lying on the ground as the critical events, are considered. Activity recognition is carried out using a machine learning-based classification framework with 49 and 73 features and 31 classifiers, where the effects of the number of features and variety of datasets are also studied. Experimental results demonstrate that with 49 features and using datasets from all subjects, average classification accuracies of 79.7% (training) and 80.9% (testing) can be obtained from the ensemble bagged trees. Additionally, results indicate that using datasets from all participants for training and testing results in a poorer classification accuracy than using datasets from each subject separately. For 73 features, with the ensemble bagged trees, classification accuracies are 99.5% for both training and testing. Particularly, there are 99.5% of rapid breathing, 99.7% of seizures, 97.9% of falling from the bed, and 99.8% of lying on the ground. The Fine-KNN classifier also provides satisfactory accuracies of 92.1% and 93.0%, where the prediction speeds and the training times are better.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

sEMG-based Recognition of Human Activities in Bed using a 2.4 GHz Wireless Network and Machine Learning: Impact of Feature Sizes and Dataset

  • 期刊论文

  • 发表日期：

    2026年4月2日

查看全文
  • Chawakorn Intongkum,
  • Apidet Booranawong,
  • Pornchai Phukpattaranont

摘要

This work proposes a system that uses wireless sensor nodes and surface electromyography (sEMG) data to monitor and recognize human activities in bed. The contributions of this work are that we implement 2.4 GHz IEEE 802.15.4 wireless sensor nodes combined with a human body-attached sEMG sensor, where sEMG data are wirelessly sent to a receiver connected to a computer that serves as the processing center. Human activities in bed, including rapid breathing, seizure sleeping, falling from the bed, and lying on the ground as the critical events, are considered. Activity recognition is carried out using a machine learning-based classification framework with 49 and 73 features and 31 classifiers, where the effects of the number of features and variety of datasets are also studied. Experimental results demonstrate that with 49 features and using datasets from all subjects, average classification accuracies of 79.7% (training) and 80.9% (testing) can be obtained from the ensemble bagged trees. Additionally, results indicate that using datasets from all participants for training and testing results in a poorer classification accuracy than using datasets from each subject separately. For 73 features, with the ensemble bagged trees, classification accuracies are 99.5% for both training and testing. Particularly, there are 99.5% of rapid breathing, 99.7% of seizures, 97.9% of falling from the bed, and 99.8% of lying on the ground. The Fine-KNN classifier also provides satisfactory accuracies of 92.1% and 93.0%, where the prediction speeds and the training times are better.