Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

Dublin Core

Title

Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

Subject

sensor fusion
human activity recognition
machine learning
deep learning
sensors

Description

Smart devices like smartphones and smartwatches have made this world smarter. These wearable devices are created through complex research methodologies to make them more usable and interactive with its user. Various interactive mobile applications such as augmented reality (AR), virtual reality (VR) or mixed reality (MR) applications solely depend on the in-built sensors of the smart devices. A lot of facilities can be taken from these devices with sensors such as accelerometer and gyroscope. Different physical activities such as walking, jogging, sitting, etc., can be important for analysis like health state prediction and duration of exercise by using those sensors based on artificial intelligence. In this paper, we have implemented machine learning and deep learning algorithms to detect and recognize eight activities namely, walking, jogging, standing, walking upstairs, walking downstairs, sitting, sitting-in-a-car and cycling; with a maximum of 99.3% accuracy. A few activities are almost similar in action, such as sitting and sitting-in-a-car, but difficult to distinguish; which makes it more challenging to predict tasks. In this paper, we have hypothesized that with more sensors (sensor fusion) and data collection points (sensor-body positions) a wide range of activities can be recognized and the recognition accuracies can be increased. Finally, we showed that the combination of all the sensors data of both pocket/waist and wrist can be used to recognize a wide range of activities accurately. The possibility of using the proposed methodologies for futuristic mobile technologies is quite significant. The adaptation of most recent deep learning algorithms such as convolutional neural network (CNN) and bi-directional Long Short Time Memory (Bi-LSTM) demonstrated high credibility of the methods presented as experimentation.

Creator

Alam, Ashraful
Das, Anik
Tasjid, Md. Shahriar
Marouf, Ahmed Al

Source

International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 17 (2021); pp. 141-155
1865-7923

Publisher

International Association of Online Engineering (IAOE), Vienna, Austria

Date

2021-09-06

Rights

Copyright (c) 2021 Ashraful Alam, Anik Das, Md. Shahriar Tasjid, Ahmed Al Marouf

Relation

Format

application/pdf

Language

eng

Type

info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article

Identifier

Citation

Ashraful Alam et al., Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies, International Association of Online Engineering (IAOE), Vienna, Austria, 2021, accessed November 6, 2024, https://igi.indrastra.com/items/show/2108

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