Smartphone Sensor-Based Human Locomotion Surveillance System Using Multilayer Perceptron

Abstract

Applied sensing technology has made it possible for human beings to experience a revolutionary aspect of the science and technology world. Along with many other fields in which this technology is working wonders, human locomotion activity recognition, which finds applications in healthcare, smart homes, life-logging, and many other fields, is also proving to be a landmark. The purpose of this study is to develop a novel model that can robustly handle divergent data that are acquired remotely from various sensors and make an accurate classification of human locomotion activities. The biggest support for remotely sensed human locomotion activity recognition (RS-HLAR) is provided by modern smartphones. In this paper, we propose a robust model for an RS-HLAR that is trained and tested on remotely extracted data from smartphone-embedded sensors. Initially, the system denoises the input data and then performs windowing and segmentation. Then, this preprocessed data goes to the feature extraction module where Parseval’s energy, skewness, kurtosis, Shannon entropy, and statistical features from the time domain and the frequency domain are extracted from it. Advancing further, by using Luca-measure fuzzy entropy (LFE) and Lukasiewicz similarity measure (LS)–based feature selection, the system drops the least-informative features and shrinks the feature set by 25%. In the next step, the Yeo–Johnson power transform is applied, which is a maximum-likelihood-based feature optimization algorithm. The optimized feature set is then forwarded to the multilayer perceptron (MLP) classifier that performs the classification. MLP uses the cross-validation technique for training and testing to generate reliable results. We designed our system while experimenting on three benchmark datasets namely, MobiAct_v2.0, Real-World HAR, and Real-Life HAR. The proposed model outperforms the existing state-of-the-art models by scoring a mean accuracy of 84.49% on MobiAct_v2.0, 94.16% on Real-World HAR, and 95.89% on Real-Life HAR. Although our system can accurately differentiate among similar activities, excessive noise in data and complex activities have shown an inverse effect on its performance.

A method for human behavior identification based on integrated sensor data using XGBoost classifier with PCA techniques

“…In [48], authors have proposed the Att-CNN-LSTM model on sensor-based HAR and achieved an accuracy of 95.76%. In [49], authors have worked on three data sets MobiAct_v2.0, Real-World HAR, and Real-Life HAR. They have applied MLP on these data sets and achieved mean accuracy of 84.5%, 94.2%, and 95.9% respectively.…”

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

“…The peak detection is anchored on identifying local maxima in the magnitude signal that stand out from their surroundings. The step detected [79] for indoor and outdoor activities can be seen in Figure 8. Step Detection To understand the steps [74][75][76][77][78] from accelerometer data, we harness the magnitude of the acceleration vector.…”

“…The peak detection is anchored on identifying local maxima in the magnitude signal that stand out from their surroundings. The step detected [79] for indoor and outdoor activities can be seen in Figure 8.…”

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