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Wearable sensor-based pattern mining for human activity recognition: deep learning approach
Industrial Robot ( IF 1.8 ) Pub Date : 2021-08-16 , DOI: 10.1108/ir-09-2020-0187
Vishwanath Bijalwan 1 , Vijay Bhaskar Semwal 2 , Vishal Gupta 3
Affiliation  

Purpose

This paper aims to deal with the human activity recognition using human gait pattern. The paper has considered the experiment results of seven different activities: normal walk, jogging, walking on toe, walking on heel, upstairs, downstairs and sit-ups.

Design/methodology/approach

In this current research, the data is collected for different activities using tri-axial inertial measurement unit (IMU) sensor enabled with three-axis accelerometer to capture the spatial data, three-axis gyroscopes to capture the orientation around axis and 3° magnetometer. It was wirelessly connected to the receiver. The IMU sensor is placed at the centre of mass position of each subject. The data is collected for 30 subjects including 11 females and 19 males of different age groups between 10 and 45 years. The captured data is pre-processed using different filters and cubic spline techniques. After processing, the data are labelled into seven activities. For data acquisition, a Python-based GUI has been designed to analyse and display the processed data. The data is further classified using four different deep learning model: deep neural network, bidirectional-long short-term memory (BLSTM), convolution neural network (CNN) and CNN-LSTM. The model classification accuracy of different classifiers is reported to be 58%, 84%, 86% and 90%.

Findings

The activities recognition using gait was obtained in an open environment. All data is collected using an IMU sensor enabled with gyroscope, accelerometer and magnetometer in both offline and real-time activity recognition using gait. Both sensors showed their usefulness in empirical capability to capture a precised data during all seven activities. The inverse kinematics algorithm is solved to calculate the joint angle from spatial data for all six joints hip, knee, ankle of left and right leg.

Practical implications

This work helps to recognize the walking activity using gait pattern analysis. Further, it helps to understand the different joint angle patterns during different activities. A system is designed for real-time analysis of human walking activity using gait. A standalone real-time system has been designed and realized for analysis of these seven different activities.

Originality/value

The data is collected through IMU sensors for seven activities with equal timestamp without noise and data loss using wirelessly. The setup is useful for the data collection in an open environment outside the laboratory environment for activity recognition. The paper also presents the analysis of all seven different activity trajectories patterns.



中文翻译:

基于可穿戴传感器的人类活动识别模式挖掘:深度学习方法

目的

本文旨在利用人类步态模式处理人类活动识别。该论文考虑了七种不同活动的实验结果:正常步行、慢跑、脚趾走路、脚后跟走路、上楼、下楼和仰卧起坐。

设计/方法/方法

在目前的研究中,使用三轴惯性测量单元 (IMU) 传感器收集不同活动的数据,该传感器配备三轴加速度计来捕获空间数据,三轴陀螺仪来捕获绕轴的方向和 3° 磁力计。它以无线方式连接到接收器。IMU 传感器放置在每个对象的质心位置。收集了 30 名受试者的数据,其中包括 11 名女性和 19 名男性,年龄在 10 至 45 岁之间。捕获的数据使用不同的过滤器和三次样条技术进行预处理。处理后,数据被标记为七个活动。对于数据采集,设计了基于 Python 的 GUI 来分析和显示处理后的数据。使用四种不同的深度学习模型对数据进行进一步分类:深度神经网络、双向长短期记忆 (BLSTM)、卷积神经网络 (CNN) 和 CNN-LSTM。据报道,不同分类器的模型分类准确率分别为 58%、84%、86% 和 90%。

发现

使用步态的活动识别是在开放环境中获得的。所有数据都是使用带陀螺仪、加速度计和磁力计的 IMU 传感器收集的,可使用步态进行离线和实时活动识别。两种传感器都显示了它们在所有七项活动中捕获精确数据的经验能力方面的有用性。求解逆运动学算法,根据空间数据计算左右腿髋、膝、踝六个关节的关节角度。

实际影响

这项工作有助于使用步态模式分析识别步行活动。此外,它有助于了解不同活动期间的不同关节角度模式。系统设计用于使用步态实时分析人类步行活动。已经设计并实现了一个独立的实时系统,用于分析这七种不同的活动。

原创性/价值

通过 IMU 传感器收集七项活动的数据,这些活动具有相同的时间戳,使用无线方式没有噪音和数据丢失。该设置对于在实验室环境之外的开放环境中收集数据以进行活动识别很有用。该论文还介绍了对所有七种不同活动轨迹模式的分析。

更新日期:2021-08-16
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