scholarly journals A Resource Constrained Neural Network for the Design of Embedded Human Posture Recognition Systems

2021 ◽  
Vol 11 (11) ◽  
pp. 4752
Author(s):  
Gian Domenico Licciardo ◽  
Alessandro Russo ◽  
Alessandro Naddeo ◽  
Nicola Cappetti ◽  
Luigi Di Benedetto ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Dissipation ◽  
Pressure Sensors ◽  
Quantization Scheme ◽  
Sitting Posture ◽  
Human Posture ◽  
Computing Paradigm ◽  
Low Power Dissipation ◽  
Posture Recognition ◽  
Public Dataset

A custom HW design of a Fully Convolutional Neural Network (FCN) is presented in this paper to implement an embeddable Human Posture Recognition (HPR) system capable of very high accuracy both for laying and sitting posture recognition. The FCN exploits a new base-2 quantization scheme for weight and binarized activations to meet the optimal trade-off between low power dissipation, a very reduced set of instantiated physical resources and state-of-the-art accuracy to classify human postures. By using a limited number of pressure sensors only, the optimized HW implementation allows keeping the computation close to the data sources according to the edge computing paradigm and enables the design of embedded HP systems. The FCN can be simply reconfigured to be used for laying and sitting posture recognition. Tested on a public dataset for in-bed posture classification, the proposed FCN obtains a mean accuracy value of 96.77% to recognize 17 different postures, while a small custom dataset has been used for training and testing for sitting posture recognition, where the FCN achieves 98.88% accuracy to recognize eight positions. The FCN has been prototyped on a Xilinx Artix 7 FPGA where it exhibits a dynamic power dissipation lower than 11 mW and 7 mW for laying and sitting posture recognition, respectively, and a maximum operation frequency of 47.64 MHz and 26.6 MHz, corresponding to an Output Data Rate (ODR) of the sensors of 16.50 kHz and 9.13 kHz, respectively. Furthermore, synthesis results with a CMOS 130 nm technology have been reported, to give an estimation about the possibility of an in-sensor circuital implementation.

scholarly journals Adiabatic superconducting cells for ultra-low-power artificial neural networks

2016 ◽  
Vol 7 ◽  
pp. 1397-1403 ◽  
Author(s):  
Andrey E Schegolev ◽  
Nikolay V Klenov ◽  
Igor I Soloviev ◽  
Maxim V Tereshonok
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Low Power ◽  
Power Dissipation ◽  
Radial Basis Function Networks ◽  
Activation Functions ◽  
Ultra Low Power ◽  
Network Algorithms ◽  
Low Power Dissipation

We propose the concept of using superconducting quantum interferometers for the implementation of neural network algorithms with extremely low power dissipation. These adiabatic elements are Josephson cells with sigmoid- and Gaussian-like activation functions. We optimize their parameters for application in three-layer perceptron and radial basis function networks.

Sitting Posture Recognition Using a Spiking Neural Network

2021 ◽  
Vol 21 (2) ◽  
pp. 1779-1786 ◽  
Author(s):  
Jianquan Wang ◽  
Basim Hafidh ◽  
Haiwei Dong ◽  
Abdulmotaleb El Saddik
Keyword(s):  
Neural Network ◽  
Spiking Neural Network ◽  
Sitting Posture ◽  
Posture Recognition

scholarly journals Saliency based human fall detection in smart home environments using posture recognition

2021 ◽  
Vol 27 (3) ◽  
pp. 146045822110309
Author(s):  
Mikael Ange Mousse ◽  
Béthel Atohoun
Keyword(s):  
Monitoring System ◽  
Smart Home ◽  
Fall Detection ◽  
Human Posture ◽  
Bounding Volume ◽  
Research Theme ◽  
Smart Home Environment ◽  
Posture Recognition ◽  
Public Dataset ◽  
Modelling Approach

The implementation of people monitoring system is an evolving research theme. This paper introduces an elderly monitoring system that recognizes human posture from overlapping cameras for people fall detection in a smart home environment. In these environments, the zone of movement is limited. Our approach used this characteristic to recognize human posture fastly by proposing a region-wise modelling approach. It classifies persons pose in four groups: standing, crouching, sitting and lying on the floor. These postures are obtained by calculating an estimation of the human bounding volume. This volume is estimated by obtaining the height of the person and its surface that is in contact with the ground according to the foreground information of each camera. Using them, we distinguish each postures and differentiate lying on floor posture, which can be considered as the falling posture from other postures. The global multiview information of the scene is obtaining by using homographic projection. We test our proposed algorithm on multiple cameras based fall detection public dataset and the results prove the efficiency of our method.

scholarly journals A Control and Posture Recognition Strategy for Upper-Limb Rehabilitation of Stroke Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xian Yu ◽  
Bo Xiao ◽  
Ye Tian ◽  
Zihao Wu ◽  
Qi Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Acquisition ◽  
Network Model ◽  
Upper Limb ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Upper Limb Rehabilitation ◽  
Human Posture ◽  
Posture Recognition ◽  
Limb Posture

At present, the study of upper-limb posture recognition is still in the primary stage; due to the diversity of the objective environment and the complexity of the human body posture, the upper-limb posture has no public dataset. In this paper, an upper extremity data acquisition system is designed, with a three-channel data acquisition mode, collect acceleration signal, and gyroscope signal as sample data. The datasets were preprocessed with deweighting, interpolation, and feature extraction. With the goal of recognizing human posture, experiments with KNN, logistic regression, and random gradient descent algorithms were conducted. In order to verify the superiority of each algorithm, the data window was adjusted to compare the recognition speed, computation time, and accuracy of each classifier. For the problem of improving the accuracy of human posture recognition, a neural network model based on full connectivity is developed. In addition, this paper proposes a finite state machine- (FSM-) based FES control model for controlling the upper limb to perform a range of functional tasks. In the process of constructing the network model, the effects of different hidden layers, activation functions, and optimizers on the recognition rate were experimental for the comparative analysis; the softplus activation function with better recognition performance and the adagrad optimizer are selected. Finally, by comparing the comprehensive recognition accuracy and time efficiency with other classification models, the fully connected neural network is verified in the human posture superiority in identification.

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