HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

Abstract

The daily life-log routines of elderly individuals are susceptible to numerous complications in their physical healthcare patterns. Some of these complications can cause injuries, followed by extensive and expensive recovery stages. It is important to identify physical healthcare patterns that can describe and convey the exact state of an individual’s physical health while they perform their daily life activities. In this paper, we propose a novel Sustainable Physical Healthcare Pattern Recognition (SPHR) approach using a hybrid features model that is capable of distinguishing multiple physical activities based on a multiple wearable sensors system. Initially, we acquired raw data from well-known datasets, i.e., mobile health and human gait databases comprised of multiple human activities. The proposed strategy includes data pre-processing, hybrid feature detection, and feature-to-feature fusion and reduction, followed by codebook generation and classification, which can recognize sustainable physical healthcare patterns. Feature-to-feature fusion unites the cues from all of the sensors, and Gaussian mixture models are used for the codebook generation. For the classification, we recommend deep belief networks with restricted Boltzmann machines for five hidden layers. Finally, the results are compared with state-of-the-art techniques in order to demonstrate significant improvements in accuracy for physical healthcare pattern recognition. The experiments show that the proposed architecture attained improved accuracy rates for both datasets, and that it represents a significant sustainable physical healthcare pattern recognition (SPHR) approach. The anticipated system has potential for use in human–machine interaction domains such as continuous movement recognition, pattern-based surveillance, mobility assistance, and robot control systems.

An LSTM-Based Approach for Understanding Human Interactions Using Hybrid Feature Descriptors Over Depth Sensors

“…Some researchers [29,30] also prefer extracting various features and then combining them since hybrid features have yielded better classification results in the past. For example, A. Jalal et al [31] combined four different types of features including blobs, multiple orientations, Fourier transforms, and geometrical points.…”

Multi-Class Classification of Medical Data Based on Neural Network Pruning and Information-Entropy Measures

“…This lack of intra-layer connections allows the weights of the connections between the visible and hidden nodes to be learned by Hinton’s contrastive divergence algorithm [ 11 ]. In conjunction with deep belief networks, these neural networks have been applied to address several problems, for example, natural language processing [ 12 ], image classification [ 13 ], forecasting time series [ 14 ], and voice synthesis [ 15 ], among others.…”

MS-DLD: Multi-Sensors Based Daily Locomotion Detection via Kinematic-Static Energy and Body-Specific HMMs

“…However, the drawback of this effort is that only young subjects, ranging in age from 23 to 29 years, were studied. In [13], Javeed et al presented hybrid features for a sustainable physical healthcare patterns recognition model via different body-worn sensors. Moreover, the method has used IMU, EMG, and ECG signals to be filtered and windowed followed by features extraction, fusion, and selection techniques.…”

“…The study discussed in this paper proposed a new technique of classifying activities and developed body-specific modified HMMs. To test the performance of these two methods, this study recommended cross-validation with 10-folds [13] on three benchmark datasets: UP-Fall [14], IM-WSHA [15], and ENABL3S [16]. The IMUs data acquired from UP-Fall dataset has been pre-processed using a state-of-the-art wavelet transformed Quaternion (WTQ)-based modified filter.…”