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

2020 ◽  
pp. 375-385
Author(s):  
Achraf Jmal ◽  
Rim Barioul ◽  
Amel Meddeb Makhlouf ◽  
Ahmed Fakhfakh ◽  
Olfa Kanoun
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Inertial Sensor ◽  
Human Activity Recognition ◽  
Raspberry Pi

scholarly journals Iss2Image: A Novel Signal-Encoding Technique for CNN-Based Human Activity Recognition

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3910 ◽  
Author(s):  
Taeho Hur ◽  
Jaehun Bang ◽  
Thien Huynh-The ◽  
Jongwon Lee ◽  
Jee-In Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Expert Knowledge ◽  
Inertial Sensor ◽  
Human Activity Recognition ◽  
Learning Technology ◽  
Sensor Signal ◽  
Signal Encoding ◽  
The Right

The most significant barrier to success in human activity recognition is extracting and selecting the right features. In traditional methods, the features are chosen by humans, which requires the user to have expert knowledge or to do a large amount of empirical study. Newly developed deep learning technology can automatically extract and select features. Among the various deep learning methods, convolutional neural networks (CNNs) have the advantages of local dependency and scale invariance and are suitable for temporal data such as accelerometer (ACC) signals. In this paper, we propose an efficient human activity recognition method, namely Iss2Image (Inertial sensor signal to Image), a novel encoding technique for transforming an inertial sensor signal into an image with minimum distortion and a CNN model for image-based activity classification. Iss2Image converts real number values from the X, Y, and Z axes into three color channels to precisely infer correlations among successive sensor signal values in three different dimensions. We experimentally evaluated our method using several well-known datasets and our own dataset collected from a smartphone and smartwatch. The proposed method shows higher accuracy than other state-of-the-art approaches on the tested datasets.

scholarly journals Human Activity Recognition for Indoor Localization Using Smartphone Inertial Sensors

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6316
Author(s):  
Dinis Moreira ◽  
Marília Barandas ◽  
Tiago Rocha ◽  
Pedro Alves ◽  
Ricardo Santos ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Short Term Memory ◽  
Indoor Localization ◽  
Inertial Sensor ◽  
Human Activity Recognition ◽  
Location Based Services ◽  
Sensor Data ◽  
Indoor Environments

With the fast increase in the demand for location-based services and the proliferation of smartphones, the topic of indoor localization is attracting great interest. In indoor environments, users’ performed activities carry useful semantic information. These activities can then be used by indoor localization systems to confirm users’ current relative locations in a building. In this paper, we propose a deep-learning model based on a Convolutional Long Short-Term Memory (ConvLSTM) network to classify human activities within the indoor localization scenario using smartphone inertial sensor data. Results show that the proposed human activity recognition (HAR) model accurately identifies nine types of activities: not moving, walking, running, going up in an elevator, going down in an elevator, walking upstairs, walking downstairs, or going up and down a ramp. Moreover, predicted human activities were integrated within an existing indoor positioning system and evaluated in a multi-story building across several testing routes, with an average positioning error of 2.4 m. The results show that the inclusion of human activity information can reduce the overall localization error of the system and actively contribute to the better identification of floor transitions within a building. The conducted experiments demonstrated promising results and verified the effectiveness of using human activity-related information for indoor localization.

scholarly journals Performance Comparisson Human Activity Recognition Using Simple Linear Method

2020 ◽  
pp. 1-10 ◽  
Author(s):  
Wahyu Andhyka Kusuma ◽  
Zamah Sari ◽  
Agus Eko Minarno ◽  
Hardianto Wibowo ◽  
Denar Regata Akbi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Activity Recognition ◽  
Human Activity ◽  
Daily Activity ◽  
Inertial Sensor ◽  
Linear Method ◽  
Human Activity Recognition ◽  
Support Vector ◽  
Physical Analysis

Human activity recognition (HAR) with daily activities have become leading problems in human physical analysis. HAR with wide application in several areas of human physical analysis were increased along with several machine learning methods. This topic such as fall detection, medical rehabilitation or other smart appliance in physical analysis application has increase degree of life. Smart wearable devices with inertial sensor accelerometer and gyroscope were popular sensor for physical analysis. The previous research used this sensor with a various position in the human body part. Activities can classify in three class, static activity (SA), transition activity (TA), and dynamic activity (DA). Activity from complexity in activities can be separated in low and high complexity based on daily activity. Daily activity pattern has the same shape and patterns with gathering sensor. Dataset used in this paper have acquired from 30 volunteers.  Seven basic machine learning algorithm Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosted and K-Nearest Neighbor. Confusion activities were solved with a simple linear method. The purposed method Logistic Regression achieves 98% accuracy same as SVM with linear kernel, with same result hyperparameter tuning for both methods have the same accuracy. LR and SVC its better used in SA and DA without TA in each recognizing.

Inertial Sensor Based Human Activity Recognition via Reduced Kernel PCA

2018 ◽  
pp. 447-456 ◽  
Author(s):  
Donghui Wu ◽  
Huanlong Zhang ◽  
Cong Niu ◽  
Jing Ren ◽  
Wanwan Zhao
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Inertial Sensor ◽  
Human Activity Recognition ◽  
Kernel Pca

scholarly journals A Method of Human Activity Recognition in Transitional Period

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 416 ◽  
Author(s):  
Lei Chen ◽  
Shurui Fan ◽  
Vikram Kumar ◽  
Yating Jia
Keyword(s):  
Support Vector Machine ◽  
Activity Recognition ◽  
Human Activity ◽  
Inertial Sensor ◽  
Human Activity Recognition ◽  
Transitional Period ◽  
Sensor Data ◽  
Support Vector ◽  
Feature Subset ◽  
Posture Classification

Human activity recognition (HAR) has been increasingly used in medical care, behavior analysis, and entertainment industry to improve the experience of users. Most of the existing works use fixed models to identify various activities. However, they do not adapt well to the dynamic nature of human activities. We investigated the activity recognition with postural transition awareness. The inertial sensor data was processed by filters and we used both time domain and frequency domain of the signals to extract the feature set. For the corresponding posture classification, three feature selection algorithms were considered to select 585 features to obtain the optimal feature subset for the posture classification. And We adopted three classifiers (support vector machine, decision tree, and random forest) for comparative analysis. After experiments, the support vector machine gave better classification results than other two methods. By using the support vector machine, we could achieve up to 98% accuracy in the Multi-class classification. Finally, the results were verified by probability estimation.

scholarly journals An Energy-Efficient Human Activity Recognition System Based on Smartphones

2020 ◽  
Author(s):  
Junhao Shi ◽  
Decheng Zuo ◽  
Zhan Zhang ◽  
Daohua Pan
Keyword(s):  
Time Delay ◽  
Activity Recognition ◽  
Human Activity ◽  
Learning Algorithm ◽  
Inertial Sensor ◽  
Frequency Control ◽  
Human Activity Recognition ◽  
Research Field ◽  
Smart Devices ◽  
Battery Life

Abstract Smartphone-based human activity recognition has become a considerable research field as a subdomains of pattern recognition and pervasive computing. With the increasing popularity of smartphones, HAR has prominent applications in number of fields such as health care, education, entertainment and etc. Smart devices have a huge advantage in convenience as the main acquisition and processing equipment, but the battery life of smartphone and other resources are limited for long-duration tasks. In this paper, we propose a lightweight HAR system. The system realizes HAR algorithm with deep learning algorithm. Beyond that, we introduce a clustering-center based pre-classification strategy to reduce the call frequency of the DL model. Meanwhile, we add a sampling frequency control mechanism to the inertial sensor. The goal of the whole system is to achieve low power consumption and time delay. According to the final experiment results, the energy consumption reduces about 49% and time delay reduces about 55% while the overall recognition accuracy only suffers about 10% reduction.

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