Types Of Human Motions Research Articles

A fabric-based electrode for wearable piezoelectric nanogenerators to distinguish sense human motions

To develop a PENG with both good comfort and high performance, we designed a wearable Ag/PEI/Cotton fabric electrode. Herein, PEI was used as a bridge to connect the Ag nanoparticles with cotton fabric by the LbL self-assembly method. The sheet resistance of the Ag/PEI/Cotton fabric electrode reached 1.6 × 10−4 Ω·cm. We found that the Ag/PEI/Cotton fabric electrode was more flexible and had better mechanical properties than the Cu metal electrode. The Ag/PEI/Cotton fabric electrode also showed good antibacterial activity, suggesting that it could be safely used as a fabric in contact with human skin. Since the Ag/PEI/Cotton fabric electrode had a low sheet resistance, it could be used as a replacement for the metal electrode traditionally used in composite PENGs. The output voltage and output current of the BT/PVDF-Ag/PEI/Cotton composite PENG were 1.8 V and 0.37 μA, respectively, and the output voltage was stable after 1500 cycles. The composite PENG prepared with the Ag/PEI/Cotton fabric electrode could sense various types of human motion. Herein, we provide a new method to fabricate a wearable fabric-based electrode, that can replace the conventional metal electrode to prepare composite PENGs. The BT/PVDF-Ag/PEI/Cotton composite PENG has extensive application prospects in motion monitoring and wearable devices.

An annular tubular wearable piezoelectric-electromagnetic hybrid vibration energy harvester driven by multi magnetic beads

Wearable devices are widely applied because of the rapid development of semiconductor devices and integrated circuit technology. People hoped to enhance their senses through various wearable devices. However, the application is currently limited by the operating time of electrochemical batteries. This paper presents an annular tubular wearable piezoelectric-electromagnetic hybrid vibration energy harvester driven by multi magnetic beads for harvesting energy from the arm swing motion. The structure consists of a piezoelectric beam, magnetic beads and a set of coils. Specifically, piezoelectric energy harvesting (PEH) converts vibration energy into electrical energy by using a new macro-fiber composite (MFC). Magnetic beads and coils form an electromagnetic energy harvester (EMEH).Two energy harvesting units are coupled by magnetic beads. The magnetic beads can not only induce the coil to generate electrical energy, but also excite the piezoelectric beam. Two conversion mechanisms extract energy simultaneously through one excitation.Experimental results show that the device can harvest the vibration energy generated by human activity under different types of human motion, such as slow walking, fast walking and running. The integration between PEH and EMEH not only extends the operating bandwidth, but also makes it easy for ultra-low frequency energy harvesting. The prototype harvester that could power a number of portable electronic devices shows great potential.

A Chewing Gum Residue-Based Gel with Superior Mechanical Properties and Self-Healability for Flexible Wearable Sensor.

Chewing gum residue is hard to decompose and easy to cause pollution, which is highly desirable to realize recycling. In this paper, a chewing gum gel with enhanced mechanical properties and self-healing properties is prepared by using polyvinyl alcohol (PVA) as the backbone in chewing gum residue. The hydrogen bond and the borax ester bond are employed to construct reversible interaction to enhance the self-healing ability. The physical crosslinking is realized by further freeze-thaw treatment to improve its mechanical properties. The gel demonstrates high elongation at break of 610% and strength of 0.11MPa, as well as excellent self-healing performance and recyclable properties. In particular, the gel with a fast signal response is successfully applied as a wearable strain sensor to monitor different types of human motion. The gel as a sensor exhibits self-healing properties suggesting superior safety and stability, and displays wide linear sensitivity (the gauge factor is 0.417 and 0.170). The gel can be further served to explore temperature changes, implying the application in temperature monitoring. This study develops a novel approach for the recycle and reuse of chewing gum residue. The obtained gel may be a promising candidate for the fabrication of flexible wearable sensor.

Flexible Bielectrode‐Based Highly Sensitive Triboelectric Motion Sensor: A Sustainable and Smart Electronic Material

The self‐powered and autonomous sensors are incredibly important in advanced engineering, especially defence science. The increasing necessity of simple and smart electronics requires to be sustainably flexible, wearable, and waterproof. Triboelectricity has been a widely used mechanism for motion sensing nowadays. Almost all devices based on triboelectricity require contact between two surfaces. Herein, a touchless triboelectric motion sensor for human motion sensing and movement monitoring is developed. The device was primarily fabricated using simple latex (cis‐1,4‐polyisoprene) structures and copper (electrode materials), which make it a very cost‐effective device for sensory applications. The device is tested with specimens of different areas and heights in motion. The maximum output of the device is noted as 12 V at a specimen height of 5 cm. Further different types of human motions are applied in front of the device to ensure low energy sensitivity using triboelectric phenomena. The lightweight smart device precisely provides significant output signals for each movement of the human body which makes the device a prospective medium for motion sensing and movement monitoring which can be applied in the fields of security, energy, and medicine.

High performance piezoelectric nanogenerator with silver nanowires embedded in polymer matrix for mechanical energy harvesting

Organic-inorganic piezoelectric nanogenerators (PNGs) have attracted extensive attention for transforming mechanical energy into electricity in the field of self-powered wearables. Generally, the output electrical performance of PNGs can be enhanced by choosing various materials. Herein, the flexible PNG is fabricated by embedding the Sm-doped Pb(Mg 1/3 Nb 2/3 )O 3 –PbTiO 3 (Sm-PMN-PT) polycrystalline piezoelectric ceramics into polyvinylidene fluoride-trifluoroethylene (P(VDF-TrFE)) composite film. The ceramics with high piezoelectric voltage coefficient g 33 of Sm-PMN-PT are selected as piezoelectric fillers, and the silver nanowires (Ag NWs) with high aspect ratio are embedded as conductive fillers. The optimized electrical properties are achieved for the Ag NWs/15 wt%Sm-PMN-PT/P(VDF-TrFE) PNG, with the open-circuit voltage of up to 83.5 V and instantaneous power density of 7.48 μW/cm 2 . In particular, the good mechanical stability up to 6000 bending–releasing cycles are obtained after aging for two months. The finite element analysis is utilized to calculate the electric potential of piezoelectric composite materials. Moreover, the Ag NWs/15 wt%Sm-PMN-PT/P(VDF-TrFE) PNG could harvest mechanical energy from different types of human motion, such as taping, bending and pressing. All these results indicated that the Ag NWs/15 wt%Sm-PMN-PT/P(VDF-TrFE) PNG can serve as a promising candidate for the harvest mechanical energy in the surrounding environment.

Solution-Processable Conductive Composite Hydrogels with Multiple Synergetic Networks toward Wearable Pressure/Strain Sensors.

A biocompatible, flexible, yet robust conductive composite hydrogel (CCH) for wearable pressure/strain sensors has been achieved by an all-solution-based approach. The CCH is rationally constructed by in situ polymerization of aniline (An) monomers in the polyvinyl alcohol (PVA) matrix, followed by the cross-linking of PVA with glutaraldehyde (GA) as the cross-linker. The unique multiple synergetic networks in the CCH including strong chemical covalent bonds and abundance of weak physical cross-links, i.e., hydrogen bondings and electrostatic interactions, impart excellent mechanical strength (a fracture tensile strength of 1200 kPa), superior compressibility (ε = 80%@400 kPa), outstanding stretchability (a fracture strain of 670%), high sensitivity (0.62 kPa-1 at a pressure range of 0-1.0 kPa for pressure sensing and a gauge factor of 3.4 at a strain range of 0-300% for strain sensing, respectively), and prominent fatigue resistance (1500 cycling). As the flexible wearable sensor, the CCH is able to monitor different types of human motion and diagnostically distinguish speaking. As a proof of concept, a sensing device has been designed for the real-time detection of 2D distribution of weight or pressure, suggesting its promising potentials for electronic skin, human-machine interaction, and soft robot applications.

A Skin‐Inspired Triboelectric Nanogenerator with an Interpenetrating Structure for Motion Sensing and Energy Harvesting

AbstractRapid advancements in wearable electronics impose the challenge on power supply devices. Herein, a flexible single‐electrode triboelectric nanogenerator (SE‐TENG) that enables both human motion sensing and biomechanical energy harvesting is reported. The SE‐TENG is fabricated by interpenetrating Ag‐coated polyethylene terephthalate (PET) nanofibers within a polydimethylsiloxane (PDMS) elastomer. The Ag coating and PDMS are performed as the electrode and dielectric material for the SE‐TENG, respectively. The Ag‐coated PET nanofibers enlarge the electrode surface area, which is beneficial to increase sensing sensitivity. The flexible SE‐TENG sensor shows the capability of outputting alternating electrical signals with an open‐circuit voltage up to 50 V and a short‐circuit current up to 200 nA in response to externally applied pressure. It is used to sense various types of human motions and harvest electric energy from body motion. The harvested energy can successfully power wearable electronics, such as an electronic watch and light‐emitting diode. Therefore, the as‐prepared SE‐TENG sensor with a pressure response and self‐powered capability provides potential applications in wearable sensors or flexible electronics for personal healthcare and human–machine interfaces.

Human Body Three-Dimensional Motion Skeleton Reconstruction of Moving Image Sequence

In the field of computer vision, the research of human motion analysis has a wide range of application prospects. Due to the complexity of human motion, the existing research methods have imposed many restrictions on the human body of the research object. A new method is proposed in this paper. The information of various types of human motion mainly discusses the reconstruction part of the human body's three-dimensional motion skeleton. The basic idea is to establish the image of each image based on the camera calibration and the application of three-dimensional human body model knowledge and motion continuity. On the basis of obtaining the binarized motion image, automatic annotation of joint feature points is realized. Firstly, the Canny operator is used to extract the target contour, and the Douglas-Peucker vector compression algorithm is used to obtain a suitable human contour shape. According to the joint proportional structure, the position of each joint point is determined, and the joint feature points are automatically labeled. The three-dimensional human motion tracking technology is studied. The characteristic optical flow algorithm is used to track the marked joint points, and the Kalman filter is used to correct the joint points of the tracking error. The automatic labeling method of the mannequin joint points used reduces the manual intervention in the system to some extent.

Biocompatible and self-healing ionic gel skin as shape-adaptable and skin-adhering sensor of human motions

Artificial electric skin, which is capable of strongly adhering to different parts of human bodies and precisely detect different types of human motions, shows great promise for biomedical prosthetics, human/machine interfaces, wearable devices and soft robotics. In this study, biocompatible ionic gels with shape-adaptability and skin-adhering are produced through in situ polymerizing (3-acrylamidophenyl) boronic acid and acrylamide in the presence of chitosan containing catechol groups. The chemical cross-linkers are capable of modulating their elasticity, toughness and stretching tolerance. The reversible cross-linkers of H-bonding and dynamic covalent bonds endow the gels not only with strong adhering strength on different surfaces (including skin) and rapid self-healing in minutes, but also with large stretchability (e.g., 12–200 times of tensile length) and plasticity for shape-adaptability on irregular surfaces. Thus, by introducing mussel-inspired catechol groups into biomass-based macromolecules, a novel type of artificial ionic skin is designed with high sensitivity in combination with mechanical stretchability and bio-compatibility. They will promise great potential as (but not limited to) the skin-friendly sensor to detect various human motions with high accuracy and repeatability.

Motion Prediction with Artificial Neural Networks Using Wearable Strain Sensors Based on Flexible Thin Graphite Films

A human motion prediction system can be used to estimate human gestures in advance to the actual action for reducing delays in interactive system. We have already reported a method of simple and easy fabrication of strain sensors and wearable devices using pyrolytic graphite sheets (PGSs). The wearable electronics could detect various types of human motion, with high durability and fast response. In this study, we have demonstrated hand motion prediction by neural networks (NNs) using hand motion data obtained from data gloves based on PGSs. In our experiments, we measured hand motions of subjects for learning. We created 4-layered NNs to predict human hand motion in real-time. As a result, the proposed system successfully predicted hand motion in real-time. Therefore, these results suggested that human motion prediction system using NNs is able to forecast various types of human behavior using human motion data obtained from wearable devices based on PGSs.

Feature Extraction and Reconstruction by Using 2D-VMD Based on Carrier-Free UWB Radar Application in Human Motion Recognition.

As the size of the radar hardware platform becomes smaller and smaller, the cost becomes lower and lower. The application of indoor radar-based human motion recognition has become a reality, which can be realized in a low-cost device with simple architecture. Compared with narrow-band radar (such as continuous wave radar, etc.), the human motion echo signal of the carrier-free ultra-wideband (UWB) radar contains more abundant characteristic information of human motion, which is helpful for identifying different types of human motion. In this paper, a novel feature extraction method by two-dimensional variational mode decomposition (2D-VMD) algorithm is proposed. And it is used for extracting the primary features of human motion. The 2D-VMD algorithm is an adaptive non-recursive multiscale decomposition method for nonlinear and nonstationary signals. Firstly, the original 2D radar echo signals are decomposed by the 2D-VMD algorithm to capture several 2D intrinsic mode function (BIMFs) which represent different groups of central frequency components of a certain type of human motion. Secondly, original echo signals are reconstructed according to the several BIMFs, which not only have a certain inhibitory effect on the clutter in the echo signal, but can also further demonstrate that the BIMFs obtained by the 2D-VMD algorithm can represent the original 2D echo signal well. Finally, based on the measured ten different types of UWB radar human motion 2D echo analysis signals, the characteristics of these different types of human motion are extracted and the original echo signal are reconstructed. Then, the three indicators of the PCC, UQI, and PSNR between the original echo signals and extraction/reconstruction 2D signals are analyzed, which illustrate the effectiveness of 2D-VMD algorithm to extract feature of human motion 2D echo signals of the carrier-free UWB radar. Experimental results show that BIMFs by 2D-VMD algorithm can well represent the echo signal characteristics of this type of human motion, which is a very effective tool for human motion radar echo signal feature extraction.

Human motion recognition exploiting radar with stacked recurrent neural network

We develop a novel radar-based human motion recognition technique that exploits the temporal sequentiality of human motions. The stacked recurrent neural network (RNN) with long short-term memory (LSTM) units is employed to extract sequential features for automatic motion classification. The spectrogram of raw radar data is used as the network input to utilize the time-varying Doppler and micro-Doppler signatures for human motion characterization. Based on experimental data, we verified that a stacked RNN with two 36-cell LSTM layers successfully classifies six different types of human motions.

Human Motion Recognition Using E-textile Sensor and Adaptive Neuro-Fuzzy Inference System

The present paper is intended to introduce a new approach in order to classify human body movements by using textile sensor embedded fabrics. An intelligent processing model embedded in muscle activity pants has been developed based on adaptive neuro-fuzzy inference System (ANFIS) in order to recognize the types of several standard human motions. The processing circuit would digitize motion data from the fabric stretch sensor developed in previous research. Data were continuously flowed into the memory of microcontroller chip and processed in order to get important factors like as input variables of the classification model. The parameters chosen for developing the ANFIS system are the average of amplitude (AMP), the standard deviation of amplitude (STD), and the average cycle (CYC). The final decision on the types of the motions would be stored or transmitted to nearby monitoring devices. In this study, laboratory scale experiments were conducted for four different types of human motions such as walking, jumping, running, and sprinting in order to examine the feasibility of the ANFIS model developed. The accuracy of ANFIS model was compared with results of fuzzy inference system (FIS) model and artificial neural network (ANN) model. As expected, the results indicated that the adaptive neurofuzzy expert system developed could be used as one of the smart simulators in order to recognize human motions with robust and high accuracy classification rate. Based on the test statistics, ANFIS model has been proved to be superior to ANN and FIS in terms of classification rate.

Human motion recognition using SWCNT textile sensor and fuzzy inference system based smart wearable

Wearable technology through proper combination of e-textiles and wearable devices is one of the good ways to accomplish the functions required to monitor and recognize human motions in daily life. However, most of the current research was only done in just simple trials without any significant analytical algorithms. This study aims to introduce a complete combination of e-textile stretch sensor based on single-walled carbon nanotube (SWCNTs) and spandex fabric (PET/SP) with fuzzy inference system (FIS) in order to recognize human motions in a realistic applications. The wearing experiments were conducted for different types of human motions in order to examine the feasibility of the FIS model embedded in the sensing system developed. In the research, the performance of the method developed would be evaluated both by characteristics of the fabrication sensor and accuracy in the application.

Highly flexible self-powered sensors based on printed circuit board technology for human motion detection and gesture recognition

In this paper, we demonstrate a new integration of printed circuit board (PCB) technology-based self-powered sensors (PSSs) and direct-write, near-field electrospinning (NFES) with polyvinylidene fluoride (PVDF) micro/nano fibers (MNFs) as source materials. Integration with PCB technology is highly desirable for affordable mass production. In addition, we systematically investigate the effects of electrodes with intervals in the range of 0.15 mm to 0.40 mm on the resultant PSS output voltage and current. The results show that at a strain of 0.5% and 5 Hz, a PSS with a gap interval 0.15 mm produces a maximum output voltage of 3 V and a maximum output current of 220 nA. Under the same dimensional constraints, the MNFs are massively connected in series (via accumulation of continuous MNFs across the gaps ) and in parallel (via accumulation of parallel MNFs on the same gap) simultaneously. Finally, encapsulation in a flexible polymer with different interval electrodes demonstrated that electrical superposition can be realized by connecting MNFs collectively and effectively in serial/parallel patterns to achieve a high current and high voltage output, respectively. Further improvement in PSSs based on the effect of cooperativity was experimentally realized by rolling-up the device into a cylindrical shape, resulting in a 130% increase in power output due to the cooperative effect. We assembled the piezoelectric MNF sensors on gloves, bandages and stockings to fabricate devices that can detect different types of human motion, including finger motion and various flexing and extensions of an ankle. The firmly glued PSSs were tested on the glove and ankle respectively to detect and harvest the various movements and the output voltage was recorded as ∼1.5 V under jumping movement (one PSS) and ∼4.5 V for the clenched fist with five fingers bent concurrently (five PSSs). This research shows that piezoelectric MNFs not only have a huge impact on harvesting various external sources from mechanical energy but also can distinguish different motions as a self-powered active deformation sensor.

Determining the Reaction Forces and Torques that Appeared in the Ankle Joint during Normal Walking

The human body is a very complex system which is studied by doctors but also by engineers. The human motion analysis is an important topic in the biomechanical field. It is essential to determine the forces and torques that appears in joints during daily activities for the development of implants and prosthesis. The goal of this paper is to establish time variation of force and torque in the human ankle joint during one walking step. For this experiment we used two equipments witch record the ground reaction force respectively the angular motion for the ankle joint. Based on these measurements and using the anthropometric patient parameters we developed an application (using in Matlab – Simulink software) that calculates the forces and torques that appear in the human ankle joint. The application simulates the motion taking into account the mass inertia moments. The results of simulation are the forces and torques that appear in ankle joint. The application can simulate any type of human motion, according to the input data from the excel file. These results can be used further for the optimization of ankle implants or prosthesis.

A robust system for counting people using an infrared sensor and a camera

In this paper, a multi-modal solution to the people counting problem in a given area is described. The multi-modal system consists of a differential pyro-electric infrared (PIR) sensor and a camera. Faces in the surveillance area are detected by the camera with the aim of counting people using cascaded AdaBoost classifiers. Due to the imprecise results produced by the camera-only system, an additional differential PIR sensor is integrated to the camera. Two types of human motion: (i) entry to and exit from the surveillance area and (ii) ordinary activities in that area are distinguished by the PIR sensor using a Markovian decision algorithm. The wavelet transform of the continuous-time real-valued signal received from the PIR sensor circuit is used for feature extraction from the sensor signal. Wavelet parameters are then fed to a set of Markov models representing the two motion classes. The affiliation of a test signal is decided as the class of the model yielding higher probability. People counting results produced by the camera are then corrected by utilizing the additional information obtained from the PIR sensor signal analysis. With the proof of concept built, it is shown that the multi-modal system can reduce false alarms of the camera-only system and determines the number of people watching a TV set in a more robust manner.

Modelling of human motion in interaction with sports surface – Energetic Aspects vs. Risk of Injury

AbstractThe aim in high‐performance sports activities is reaching the optimum between high efficiency and low risk of injuries during workout. This paper wants to investigate several types of human motion in interaction with a compliant ground. The influence of the surface parameters on the human metabolic cost consumption and the reaction force is analysed. (© 2012 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

A stretchable carbon nanotube strain sensor for human-motion detection

Devices made from stretchable electronic materials could be incorporated into clothing or attached directly to the body. Such materials have typically been prepared by engineering conventional rigid materials such as silicon, rather than by developing new materials. Here, we report a class of wearable and stretchable devices fabricated from thin films of aligned single-walled carbon nanotubes. When stretched, the nanotube films fracture into gaps and islands, and bundles bridging the gaps. This mechanism allows the films to act as strain sensors capable of measuring strains up to 280% (50 times more than conventional metal strain gauges), with high durability, fast response and low creep. We assembled the carbon-nanotube sensors on stockings, bandages and gloves to fabricate devices that can detect different types of human motion, including movement, typing, breathing and speech.

Three-dimensional human display model

A two-dimensional computer graphic display of a three-dimensional model depicting a human being is presented. The major body segments of the model are represented as non-uniform elliptic cylinders. The shadow outlines of these cylinders are displayed on the terminal screen and connected by circular arcs and straight lines to produce a realistic representation of a human being in any position.This human model was developed for the display of results of three-dimensional simulation programs which calculate the positions of an occupant during vehicle impact. However, it is well suited to any other type of human motion. The approach allows the user to select the viewing orientation and was designed for low cost computer and graphic terminal systems.