Human Motion Detection System Research Articles

Human Movement and Attitude Detection System Based on Flexible High Tensile Sensor of Single-Walled Carbon Nanotubes

Excellent mechanical properties and outstanding optical and electrical properties of carbon nanotubes influence the fabrication of suitable devices and micro-electronic parts and systems, and they have been broadly applied in related fields. This paper aims to study the application of flexible high-stretch sensors of single-walled carbon nanotubes in human motion and attitude detection systems. In this paper, algorithm for human motion detection is proposed according to the content of human motion posture detection, and the properties of single-walled carbon tubing are analyzed in detail. The experimental results show that with the increase of carbon nanotube tubing, the reaction speed and sensitivity increase gradually. The reaction speed and sensitivity were 39% when the content was 1.5 wt%, 42% when the content was 2.0 wt%, and 59% when the content was 2.5 wt%. In summary, it can be found that within a certain range, the increase of carbon nanotube content can effectively reduce the hysteresis error of the sensor. It makes the sensitivity of the flexible high-stretch sensor higher, thereby improving the accuracy of the human motion attitude detection system.

Evaluation of deep learning models in contactless human motion detection system for next generation healthcare

Recent decades have witnessed the growing importance of human motion detection systems based on artificial intelligence (AI). The growing interest in human motion detection systems is the advantages of automation in the monitoring of patients remotely and giving warnings to doctors promptly. Currently, wearable devices are frequently used for human motion detection systems. However, such devices have several limitations, such as the elderly not wearing devices due to lack of comfort or forgetfulness and/or battery limitations. To overcome the problems of wearable devices, we propose an AI-driven human motion detection system (deep learning-based system) using channel state information (CSI) extracted from Radio Frequency (RF) signals. The main contribution of this paper is to improve the performance of the deep learning models through techniques, including structure modification and dimension reduction of the original data. In this work, We firstly collected the CSI data with the center frequency 5.32 GHz and implemented the structure of the basic deep learning network in our previous work. After that, we changed the basic deep learning network by increasing the depth, increasing the width, adapting some advanced network structures, and reducing dimensions. After finishing those modifications, we observed the results and analyzed how to further improve the deep learning performance of this contactless AI-enabled human motion detection system. It can be found that reducing the dimension of the original data can work better than modifying the structure of the deep learning model.

MoSeFi: Duration Estimation Robust Human Motion Sensing via Commodity WiFi Device

Accurate motion interval segmentation is the basic and crucial step in the advanced human perception based on WiFi signals. However, previous works have rarely considered motion duration, which is one of the important parameters for complete description of human motion. On this basis, we deeply investigate the properties of the CSI ratio from the perspective of Mobius transformation and construct a novel motion indicator using its complementary real and imaginary parts. The new indicator can attenuate the impact of motion fragmentation under short-window conditions and significantly reduce the duration error while ensuring detection accuracy. Moreover, we propose a universal subcarrier screening method based on response sensitivity and shape similarity, which provides more accurate information for perception. Furthermore, we present MoSeFi—a duration estimation robust human motion detection system using an existing commercial WiFi device. Detailed experimental results demonstrate that MoSeFi is lightweight yet effective compared to state-of-the-art systems.

Design and Implementation of Human Motion Monitoring System on Account of Intelligent Computing of Internet of Things

The era of big data network represented mainly by the Internet of Things is limited by various factors such as various environments, volumes, and calculations. This paper proposes and studies a human motion detection system based on the intelligent computing of the Internet of Things, which can effectively detect the daily motion of the human body. In the era of the Internet of Things, this paper designs and develops a human motion detection system based on the Internet of Things technology and intelligent computing and explores the law of normal human motion. This paper also analyzes the design ideas and technical advantages of the human motion detection system from many aspects. This research is a human motion detection system designed and developed under the comprehensive use of Internet of Things technology and intelligent computing technology, which can effectively detect the actual situation of the human body in the state of motion. Experimental results show that the overall accuracy of the system for monitoring and recognizing human walking is 96.91%, and the overall accuracy of monitoring and recognizing human jogging is 97.18%. It monitors and recognizes human jogging with an overall accuracy rate of 97.96%. It has great practical significance.

Full-Textile Human Motion Detection Systems Integrated by Facile Weaving with Hierarchical Core-Shell Piezoresistive Yarns.

The tremendous progress of the wearable intelligent system has brought an urgent demand for flexible pressure sensors, especially for those possessing high sensing performances, simple manufacture technology, and efficient integration. In this work, hierarchical core-shell piezoresistive yarns (HCPYs), which contain internal silver-plated nylon electrodes and surface microporous structured carbon nanotubes (CNTs)/thermoplastic polyurethane (TPU) sensing layer, are designed and manufactured via facile wet-spinning accompanied by a water vapor coagulating bath. The obtained HCPY can either be inserted into traditional textiles to assemble a single-pressure sensor, or be woven into a textile-based flexible pressure sensors array with expected size and resolution, without compromising their comfort, breathability, and three-dimensional (3D) conformability. Simultaneously, to further enhance the sensing performance, the surface microporous structures of HCPYs are optimized by altering the treatment humidity and exposure time during the process of water vapor-induced phase separation. The wearable pressure sensors assembled by the optimal HCPY achieved a high sensitivity up to 84.5 N-1, a good durability over 5000-cycle tests, a fast response time of 2.1 ms, and a recovery time of 2.4 ms. Moreover, the wearable pressure sensors have been successfully used to monitor physical signals and human motions. The textile-based flexible pressure sensors array has also been seamlessly integrated with sportswear to detect movements of the elbow joint and map spatial pressure distribution, which makes HCPY a promising candidate for constructing next-generation wearable electronics.

Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor.

Due to the rapid aging of the population in recent years, the number of elderly people in hospitals and nursing homes is increasing, which results in a shortage of staff. Therefore, the situation of elderly citizens requires real-time attention, especially when dangerous situations such as falls occur. If staff cannot find and deal with them promptly, it might become a serious problem. For such a situation, many kinds of human motion detection systems have been in development, many of which are based on portable devices attached to a user’s body or external sensing devices such as cameras. However, portable devices can be inconvenient for users, while optical cameras are affected by lighting conditions and face privacy issues. In this study, a human motion detection system using a low-resolution infrared array sensor was developed to protect the safety and privacy of people who need to be cared for in hospitals and nursing homes. The proposed system can overcome the above limitations and have a wide range of application. The system can detect eight kinds of motions, of which falling is the most dangerous, by using a three-dimensional convolutional neural network. As a result of experiments of 16 participants and cross-validations of fall detection, the proposed method could achieve 98.8% and 94.9% of accuracy and F1-measure, respectively. They were 1% and 3.6% higher than those of a long short-term memory network, and show feasibility of real-time practical application.

Comparative study of wireless sensors for measuring the energy consumption of human running

With the rapid development of technologies such as communications, embedded computing, and sensors, wireless sensor network technology has become more mature and its applications have become more and more extensive. The development of technology has brought great convenience to people's lives and work. At the same time, people's activity and exercise are greatly reduced, causing health problems such as physical weakness. In this regard, designing a research based on wireless sensors to measure human walking and running is of great significance to promote the development of people's physical health. This paper presents a design scheme for human exercise energy consumption detection, researches and designs the coordinator, router node and host computer monitoring system of wireless sensor network. In the human motion energy consumption detection system implemented in this paper, the communication effect between the coordinator and the router node is good, and there are fewer problems such as node loss and data loss. The host computer monitoring interface is simple and easy to operate, easy to use, and can run stably. This solution does not need wiring, reduces the point of failure, reduces the cost, and has good scalability, laying a foundation for the further development of the human motion detection system, and has a wide range of application prospects and practical values. This paper is based on two constant beam width algorithms to carry out the experimental research on the energy consumption of human walking and running, and analyze the results of the energy consumption experiment. Experimental results show that the two constant beamwidth beamforming algorithms have relatively good constant beamwidth effect, which basically verifies the correctness of the theoretical analysis results and can be applied to target noise measurement.

Close-Proximity Detection for Hand Approaching Using Backscatter Communication

Smart environments and security systems require automatic detection of human behaviors including approaching to or departing from an object. Existing human motion detection systems usually require human beings to carry special devices, which limits their applications. In this paper, we present a system called APID to detect hand approaching behaviors by analyzing backscatter communication signals from a passive RFID tag on the object. APID does not require human beings to carry any device. The idea is based on the influence of hand movements to the vibration of backscattered tag signals. APID is compatible with commodity off-the-shelf devices and the EPCglobal Class-1 Generation-2 protocol. In APID, a commercial RFID reader continuously queries tags through emitting RF signals and tags simply respond with their IDs. A USRP monitor passively analyzes the communication signals and reports the approach and departure behaviors. We have implemented the APID system for both single-object and multi-object scenarios. Extensive evaluations demonstrate that APID can achieve high detection accuracy in both scenarios.

A Wrist Worn Acceleration Based Human Motion Analysis and Classification for Ambient Smart Home System

In recent years, health-care industry has received a major boost due to sensors i.e., accelerometers, magnetometers etc., which allow its user to get instant updates about their current health status in indoor/outdoor environments. The real driving force behind the usage of accelerometer has been the fitness industry but it also holds a prominent place in ambient smart home to monitor resident’s life-style. In this paper, we proposed a novel triaxial accelerometer-based human motion detection and recognition system using multiple features and random forest. Triaxial signals have been statistically processed to produce worthy features like variance, positive and negative peaks, and signal magnitude features. The proposed model was evaluated over HMP recognition data sets and achieved satisfactory recognition accuracy of 85.17%. The proposed system is directly applicable to any elderly/children health monitoring system, 3D animated games/movies and examining the indoor behaviors of people at home, malls and offices.

Multi-Target Intense Human Motion Analysis and Detection Using Channel State Information

Intense human motion, such as hitting, kicking, and falling, in some particular scenes indicates the occurrence of abnormal events like violence and school bullying. Camera-based human motion detection is an effective way to analyze human behavior and detect intense human motion. However, even if the camera is properly deployed, it will still generate blind spots. Moreover, camera-based methods cannot be used in places such as restrooms and dressing rooms due to privacy issues. In this paper, we propose a multi-target intense human motion detection scheme using commercial Wi-Fi infrastructures. Compared with human daily activities, intense human motion usually has the characteristics of intensity, rapid change, irregularity, large amplitude, and continuity. We studied the changing pattern of Channel State Information (CSI) influenced by intense human motion, and extracted features in the pattern by conducting a large number of experiments. Considering occlusion exists in some complex scenarios, we distinguished the Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions in the case of obstacles appearing between the transmitter and the receiver, which further improves the overall performance. We implemented the intense human motion detection system using single commercial Wi-Fi devices, and evaluated it in real indoor environments. The experimental results show that our system can achieve intense human motion detection rate of 90%.

Highly Stretchable and Sensitive Strain Sensor Based on Facilely Prepared Three-Dimensional Graphene Foam Composite.

Wearable strain sensors with excellent stretchability and sensitivity have emerged as a very promising field which could be used for human motion detection and biomechanical systems, etc. Three-dimensional (3D) graphene foam (GF) has been reported before for high-performance strain sensors, however, some problems such as high cost preparation, low sensitivity, and stretchability still remain. In this paper, we report a highly stretchable and sensitive strain sensor based on 3D GF and polydimethylsiloxane (PDMS) composite. The GF is prepared by assembly process from graphene oxide via a facile and scalable method and possesses excellent mechanical property which facilitates the infiltration of PDMS prepolymer into the graphene framework. The as-prepared strain sensor can be stretched as high as 30% of its original length and the gauge factor of this sensor is as high as 98.66 under 5% of applied strain. Moreover, the strain sensor shows long-term stability in 200 cycles of stretching-relaxing. Implementation of the device for monitoring the bending of elbow and finger results in reproducibility and various responses in the form of resistance change. Thus, the developed strain sensors exhibit great application potential in fields of biomechanical systems and human-interactive applications.

Detection and tracking of human movement based on the motion vectors estimation

This paper proposes a human and vehicle motion detection and tracking system, based on motion vectors, which is able to discriminate between allowed and not allowed movements without the intervention of any human operator. Evaluation results show the desirable features of proposed method.

무선센서 기반 다차원 사용자 움직임 탐지 시스템

Due to recently advanced electrical devices, human can access computer network regardless of working location or time restriction. However, currently widely used mouse, joystick, and trackball input system are not easy to carry and they bound user hands exclusively within working space. Those make user inconvenient in Ubiquitous environments.. In this paper, we propose multiple dimension human motion detection system based on wireless sensor networks. It is a portable input device and provides easy installation process and unbinds user hands during input processing stages. Our implemented system is comprised of three components. One is input unit that senses user motions and transmits collected data to receiver. Second is receiver that conveys the received data to application, which runs on server computer. Third is application that performs command operations according to received data. Experiments shows that proposed system accurately detect the characteristics of user arm motions and fully support corresponding input requests.

An Analysis of Human Motion Detection Systems Use During Elder Exercise Routines

Human motion analysis provides motion pattern and body pose estimations. This study integrates computer-vision techniques and explores a markerless human motion analysis system. Using human-computer interaction (HCI) methods and goals, researchers use a computer interface to provide feedback about range of motion to users. A total of 35 adults aged 65 and older perform three exercises in a public gym while human motion capture methods are used. Following exercises, participants are shown processed human motion images captured during exercises on a customized interface. Standardized questionnaires are used to elicit responses from users during interactions with the interface. A matrix of HCI goals (effectiveness, efficiency, and user satisfaction) and emerging themes are used to describe interactions. Sixteen users state the interface would be useful, but not necessarily for safety purposes. Users want better image quality, when expectations are matched satisfaction increases, and unclear meaning of motion measures decreases satisfaction.

A System Concept for 3D Human Flow Management Based on 3D Spatial Information

abstractA controller who is responsible for safety makes decisions concerning measures for visitor safety using a human-based decision-making process. Many potential accidents that could be caused by human error lurk in the process. These accidents can be reduced by changing from a human-based to technology based decision-making process. A technology-based decision-making process can draw a controller’s attention to trouble spots by data filtering, alarm filtering, and so on. The controller can thus obtain information on the occurrence of possible accidents proactively.The objective of this study is to suggest a system for 3D human flow management using a technology-based decision-making process. The system is divided into two parts: a 3D spatial information system and a human motion detection system. The former is built from 3D CAD and a geographic information system, and the latter is developed from image processing. This study shows the direction of system development and the method of application, and describes the contribution of the system.

Spatial and temporal selectivity of the human motion detection system

Measurements were made of spatial frequency, orientation and temporal frequency selectivity of the visual motion system. The results suggest: (1) There exists in the motion system mechanisms selective for spatial frequency. The preferred spatial frequency varies considerably and extends down to at least 0.06 c/deg. (2) At all spatial frequencies (from 0.1 to 10 c/deg) there exist detectors selective for orientation which vary in (directed) orientation tuning to encompass 360°. (3) The bandwidth of both spatial frequency and orientation selectivity vary inversely with spatial frequency: the lower the spatial frequency, the broader the bandwidth. (4) There exist two classes of temporally tuned detectors, one lowpass (sustained) and one bandpass (transient), of preferred temporal frequency of 7–13 Hz (depending on spatial frequency).

The structure of the human motion detection system

Unlike technical pattern recognizers, humans are adept at the detection of regions of coherent movements in changing images. Possible physiological mechanisms for this ability are discussed in terms of simple mechanistic models, and the results of psychophysical experiments are presented. These results are compatible with two different mechanistic interpretations. The main result is that the human movement detectors are tuned, and a whole ensemble of mechanisms, tuned to different velocities, reside at any location in the visual field. Thus an observer may easily see two velocity vectors simultaneously at a given place. Segregation occurs when different detectors are stimulated at each side of a border. The spatiotemporal parameters that characterize the units limit the resolution in time and space, whereas sensitivity depends on the number of units that participate in a detection. This number may range between a few (perhaps one) to a thousand or more. Apparently, resolution can be traded against noise immunity. It is argued that technical systems developed on a similar basis might be useful as preprocessors of sequences of images in order to detect features of interest (coherent regions) and to suggest a first rough image segmentation.