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

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

doi:10.3991/ijim.v15i17.25197

Abstract

<p class="0abstract">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.<strong></strong></p>

Highlights

We are living in the age of technological advancements
Human activity recognition is a type of work where all we have to do is to recognize or predict human movement by studying and analyzing human-computer interaction (HCI), video surveillance, wearable devices, or sensors
We have made the comparisons among accuracies obtained from different combinations of sensors and different combinations of positioning of devices if not used together

Summary

Introduction

We are living in the age of technological advancements. In this era, the field of human-centric computing research is an emerging field of research in which we can iJIM ‒ Vol 15, No 17, 2021understand the nature of human behavior, habit, interests, etc. We are living in the age of technological advancements. In this era, the field of human-centric computing research is an emerging field of research in which we can iJIM ‒ Vol 15, No 17, 2021. Human activity recognition is a type of work where all we have to do is to recognize or predict human movement by studying and analyzing human-computer interaction (HCI), video surveillance, wearable devices, or sensors. The challenging part here is to detect, predict activities with decent accuracy. We tried to recognize human activity by using in-built sensors of smartphones. Sensors give high-frequency data every second with the physical movement of different parts of the body. It is a challenging task to predict human activities from a wide range of sensor data precisely

Objectives

Methods

Results

Conclusion