CHARM-Deep: Continuous Human Activity Recognition Model Based on Deep Neural Network Using IMU Sensors of Smartwatch

计算机科学 智能手表 人工智能 惯性测量装置 深度学习 活动识别 人工神经网络 模式识别(心理学) 实时计算 嵌入式系统 可穿戴计算机
作者
Sara Ashry,Tetsuji Ogawa,Walid Gomaa
出处
期刊:IEEE Sensors Journal [IEEE Sensors Council]
卷期号:20 (15): 8757-8770 被引量:27
标识
DOI:10.1109/jsen.2020.2985374
摘要

In the present paper, an attempt was made to achieve high-performance continuous human activity recognition (CHAR) using deep neural networks. The present study focuses on recognizing different activities in a continuous stream, which means `back-to-back' consecutive set of activities, from only inertial measurement unit (IMU) sensors mounted on smartwatches. For that purpose, a new dataset called `CHAR-SW', which includes numerous streams of daily activities, was collected using smartwatches, and feature representations and network architectures were designed. Experimental comparisons using our own dataset and public datasets (Aruba and Tulum) have been performed. They demonstrated that cascading bidirectional long short-term memory (Bi-LSTM) with featured data performed well in offline mode from the viewpoints of accuracy, computational time, and storage space required. The input to the Bi-LSTM is a descriptor which composed of a stream of the following features: autocorrelation, median, entropy, and instantaneous frequency. Additionally, a novel technique to operate the CHAR system online was introduced and shown to be effective. Experimental results can be summarized as: the offline CHARM-Deep enhanced the accuracy compared with using raw data or the existing approaches, and it reduced the processing time by 86% at least relative to the time consumed in executing the Bi-LSTM classifier directly on the raw data. It also reduced storage space by approximately 97.77% compared with using raw data. The online evaluation shows that it can recognize activities in real-time with an accuracy of 91%.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
坦率的匪应助科研通管家采纳,获得10
刚刚
坦率的匪应助科研通管家采纳,获得10
刚刚
猪猪hero应助科研通管家采纳,获得10
刚刚
Zyy发布了新的文献求助30
1秒前
坦率的匪应助科研通管家采纳,获得10
1秒前
汉堡包应助科研通管家采纳,获得10
1秒前
慕青应助科研通管家采纳,获得10
1秒前
Hello应助科研通管家采纳,获得10
1秒前
慕青应助科研通管家采纳,获得10
1秒前
坦率的匪应助科研通管家采纳,获得10
1秒前
1秒前
wanci应助科研通管家采纳,获得10
1秒前
英俊的铭应助科研通管家采纳,获得10
1秒前
1秒前
1秒前
科研通AI2S应助科研通管家采纳,获得10
2秒前
2秒前
2秒前
han应助bofu采纳,获得10
3秒前
5秒前
ranjeah完成签到 ,获得积分10
6秒前
6秒前
酷波er应助123采纳,获得10
7秒前
thanhmanhp完成签到,获得积分10
7秒前
丘比特应助grisco采纳,获得10
7秒前
科研小白人完成签到 ,获得积分10
8秒前
8秒前
超级无心完成签到,获得积分10
8秒前
10秒前
puzi完成签到,获得积分10
11秒前
han应助bofu采纳,获得10
11秒前
乐乐应助十九岁的时差采纳,获得10
12秒前
12秒前
张天关注了科研通微信公众号
12秒前
vincentbioinfo完成签到,获得积分10
12秒前
斯文败类应助欣喜的成败采纳,获得10
13秒前
Zyy完成签到,获得积分10
13秒前
echoxq发布了新的文献求助10
14秒前
16秒前
852应助11号迪西馅饼采纳,获得10
16秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988838
求助须知:如何正确求助?哪些是违规求助? 3531250
关于积分的说明 11252914
捐赠科研通 3269838
什么是DOI,文献DOI怎么找? 1804820
邀请新用户注册赠送积分活动 881943
科研通“疑难数据库(出版商)”最低求助积分说明 809028