Flexible piezoelectret film sensor for noncontact mechanical signal capture by multiple transmission media

Mechanical signal capture without physical contact has emerged as a highly promising research field and attracted tremendous attention due to its prosperous applications in household medical care,lifestyle monitoring and remote operation,offering users high level of safety,convenience and comfort.Moreover,noncontact sensing is ideal to maximize the immersive user experience in the human–machine interaction(HMI),eliminating interference to human activities and mechanical fatigue to the sensor,simultaneously.Herein,we report a self-powered flexible sensor integrated with irradiation cross-linked polypropylene(IXPP)piezoelectret film for noncontact sensing,featuring multi-functions to detect mechanical signals transmitted through solid,liquid and gaseous media and would facilitate their versatile practical applications.The folded-structure configuration of the sensor facilitates the improvement of the noncontact sensing sensitivity.For solid media,such as the rectangular wooden stick used in this study,the sensor can detect mechanical stimulus exerted at a distance of 100 cm.A system detection sensitivity up to 57 pC/kPa with a low detection limit of 0.6 kPa is achieved at a noncontact distance of 10 cm.Even when partly or completely immersed in water,the sensor effectively traces movement signals of human bodies underwater,demonstrating great advantages for non-inductive aquatic fitness training monitoring.Furthermore,due to the low acoustic impedance of piezoelectret film,speech recognition through gaseous medium is also achieved.We further introduce application demonstrations of the developed film sensors to monitor exercise postures and physiological signals without direct contact between human body and the sensor,displaying great potential to be incorporated into future smart electronics.This study commendably expands the application scope of piezoelectret materials,which will have profound implications for exploring novel intelligent human–machine interactions.

Smartphone-Based Wi-Fi Analysis for Bus Passenger Counting

In the contemporary era of technological advancement,smartphones have become an indispensable part of individuals’daily lives,exerting a pervasive influence.This paper presents an innovative approach to passenger countingonbuses throughthe analysis ofWi-Fi signals emanating frompassengers’mobile devices.The study seeks to scrutinize the reliability of digital Wi-Fi environments in predicting bus occupancy levels,thereby addressing a crucial aspect of public transportation.The proposed system comprises three crucial elements:Signal capture,data filtration,and the calculation and estimation of passenger numbers.The pivotal findings reveal that the system demonstrates commendable accuracy in estimating passenger counts undermoderate-crowding conditions,with an average deviation of 20%from the ground truth and an accuracy rate ranging from 90%to 100%.This underscores its efficacy in scenarios characterized by moderate levels of crowding.However,in densely crowded conditions,the system exhibits a tendency to overestimate passenger numbers,occasionally doubling the actual count.While acknowledging the need for further research to enhance accuracy in crowded conditions,this study presents a pioneering avenue to address a significant concern in public transportation.The implications of the findings are poised to contribute substantially to the enhancement of bus operations and service quality.

A Multi-View Gait Recognition Method Using Deep Convolutional Neural Network and Channel Attention Mechanism

In many existing multi-view gait recognition methods based on images or video sequences,gait sequences are usually used to superimpose and synthesize images and construct energy-like template.However,information may be lost during the process of compositing image and capture EMG signals.Errors and the recognition accuracy may be introduced and affected respectively by some factors such as period detection.To better solve the problems,a multi-view gait recognition method using deep convolutional neural network and channel attention mechanism is proposed.Firstly,the sliding time window method is used to capture EMG signals.Then,the back-propagation learning algorithm is used to train each layer of convolution,which improves the learning ability of the convolutional neural network.Finally,the channel attention mechanism is integrated into the neural network,which will improve the ability of expressing gait features.And a classifier is used to classify gait.As can be shown from experimental results on two public datasets,OULP and CASIA-B,the recognition rate of the proposed method can be achieved at 88.44%and 97.25%respectively.As can be shown from the comparative experimental results,the proposed method has better recognition effect than several other newer convolutional neural network methods.Therefore,the combination of convolutional neural network and channel attention mechanism is of great value for gait recognition.