An Embedded ANN Raspberry PI for Inertial Sensor Based Human Activity Recognition

Achraf Jmal,Olfa Kanoun,Rim Barioul,Ahmed Fakhfakh,Amel Meddeb Makhlouf

doi:10.1007/978-3-030-51517-1_34

Achraf Jmal, Olfa Kanoun + Show 3 more

Open Access

https://doi.org/10.1007/978-3-030-51517-1_34

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Abstract

Human Activity Recognition (HAR) is one of the critical subjects of research in health and human machine interaction fields in recent years. Algorithms such as Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Decision Tree (DT) and many other algorithms were previously implemented to serve this common goal but most of the traditional Machine learning proposed solutions were not satisfying in term of accuracy and real time testing process. For that, a human activities analysis and recognition system with an embedded trained ANN model on Raspberry PI for an online testing process is proposed in this work. This paper includes a comparative study between the Artificial Neural Network (ANN) and the Recurrent Neural Network (RNN), using signals produced by the accelerometer and gyroscope, embedded within the BlueNRG-Tile sensor. After evaluate algorithms performance in terms of accuracy and precision which reached an accuracy of 82% for ANN and 99% for RNN, obtained ANN model was implemented in a Raspberry PI for real-time predictions. Results show that the system provides a real-time human activity recognition with an accuracy of 86%.

Highlights

Human activity recognition (HAR) refers to the automatic detection of various physical activities performed by people in their daily lives [1]
Sukor et al [2] used several methods of machine learning such as Support Vector Machine (SVM), Decision Tree (DT), and Multiple Layer Perception-Neural Network (MLP-NN) to classify activities such as slow sitting, standing, upstairs, downstairs and lying using the accelerometer sensor embedded in a smartphone
It could be considered that the Artificial Neural Network (ANN) is one of the best machine learning algorithm used for HAR and the Recurrent Neural Network (RNN) is reported to overperform other deep learning algorithms in term of accuracy and precision to recognize human activities

Summary

Introduction

Human activity recognition (HAR) refers to the automatic detection of various physical activities performed by people in their daily lives [1]. Three classifiers were used, including Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) and Random Forest (RF) to classify five exercises which are slow walking, normal walking, fast walking, upstairs and down stairs using accelerometer, gyroscope and magnetometer. It could be considered that the ANN is one of the best machine learning algorithm used for HAR and the RNN is reported to overperform other deep learning algorithms in term of accuracy and precision to recognize human activities. This research aims is to compare simulation results with results provided by a real-time implementation and to judge performance gived by embedded ANN to recognize human activities. To validate our simulations results, the developed ANN model is implemented in a Raspberry PI as a real-time standalone HAR system

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