Human Gait Monitoring Research Articles

Research on human gait sensing based on triboelectric nanogenerator

To address the problem of frequent battery replacement for wearable sensors applied to fall detection among the elderly, a portable and low-cost triboelectric nanogenerator (TENG)-based self-powered sensor for human gait monitoring is proposed. The main fabrication materials of the TENG are polytetrafluoroethylene (PTFE) film, aluminum (Al) foil, and polyimide (PI) film, where PTFE and Al are the friction layer materials and the PI film is used to improve the output performance. Exploiting the ability of TENGs to monitor changes in environmental conditions, a self-powered sensor based on the TENG is placed in an insole to collect gait information. Since a TENG does not require a power source to convert physical and mechanical signals into electrical signals, the electrical signals can be used as sensing signals to be analyzed by a computer to recognize daily human activities and fall status. Experimental results show that the accuracy of the TENG-based sensor for recognizing human gait is 97.2%, demonstrating superior sensing performance and providing valuable insights for future monitoring of fall events in the elderly population.

Synchronization of Separate Sensors’ Data Transferred through a Local Wi-Fi Network: A Use Case of Human-Gait Monitoring

This paper addresses the challenge of synchronizing data acquisition from independent sensor systems in a local network. The network comprises microcontroller-based systems that collect data from physical sensors used for monitoring human gait. The synchronized data are transmitted to a PC or cloud storage through a central controller. The performed research proposes a solution for effectively synchronizing the data acquisition using two alternative data-synchronization approaches. Additionally, it explores techniques to handle varying amounts of data from different sensor types. The experimental research validates the proposed solution by providing trial results and stability evaluations and comparing them to the human-gait-monitoring system requirements. The alternative data-transmission method was used to compare the data-transmission quality and data-loss rate. The developed algorithm allows data acquisition from six pressure sensors and two accelerometer/gyroscope modules, ensuring a 24.6 Hz sampling rate and 1 ms synchronization accuracy. The obtained results prove the algorithm’s suitability for human-gait monitoring under its regular activity. The paper concludes with discussions and key insights derived from the obtained results.

Robust superhydrophobic wearable piezoelectric nanogenerators for self-powered body motion sensors

Self-powered wearable sensors have attracted extensive attention in tactile sensing, motion-detecting, and biomedical signal monitoring. The practical application of these devices, however, requires not only their high sensitivity, flexibility, and breathability, but also mechanical durability and long-term resistance to humidity, liquid, and bacteria. Herein, a robust superhydrophobic antibacterial self-powered piezoelectric nanogenerator (PENG) sensor is reported. The PENG sensor is comprised of flexible electrospun poly(vinylidene fluoride-co-trifluoroethylene) piezoelectric nanofiber membrane with spray-coated interdigitated carbon nanotube electrodes, which were subsequently encapsulated by a crosslinked conformal hydrophobic nanocoating wrapping around each individual fiber through initiated chemical vapor deposition (iCVD). The obtained sensor could accurately monitor various human behaviors such as breathing and different motions. The iCVD nanocoating provides excellent protection to the sensor, imparting self-cleaning, superhydrophobicity, and more than 90% anti-fouling rate against bacteria. As a result, the sensor shows reliable and durable sensing performance under harsh environments such as high humidity, chemical exposure, and mechanical impact. Finally, owing to its extraordinary long-term durability and stability, the PENG sensor was successfully integrated into shoe insoles to realize human gait monitoring.

A flexible, stretchable and triboelectric smart sensor based on graphene oxide and polyacrylamide hydrogel for high precision gait recognition in Parkinsonian and hemiplegic patients

Intelligent gait recognition system plays an important role in the field of identity recognition, physical training and medical diagnostics. In clinical medicine, no definitive diagnostic tool has been developed for the diagnosis of Parkinson’s disease and hemiplegia. Thus, there is an urgent need to develop an effective and portable human-machine interaction system to monitor and recognize these symptoms. Herein, a self-powered strain sensor based on graphene oxide-polyacrylamide (GO-PAM) hydrogels is reported to monitor subtle human motions, including gait movements. The sensor can be used as a triboelectric nanogenerator (TENG) to collect mechanical energy. The output power of the TENG based on the 0.02 wt% GO-PAM hydrogel was up to 26 mW, which was 2.2 times that of the pure PAM hydrogel film. The capability of the TENG in powering electrical devices was demonstrated by lighting up 353 light-emitting diodes (LEDs) and powering an electronic thermometer. Besides, a wearable in-shoe monitoring system was designed which includes a flexible insole, a data processing module and a PC interface developed using Python. Among the models with different algorithms, the system with the artificial neural network (ANN) exhibits the highest recognition accuracy of 99.5 % and 98.2 % for human daily-life gait and pathological gait, respectively. This system provides a more convenient option for human gait monitoring and recognition, which can be used for a wide range of medical applications such as early diagnosis, rehabilitation evaluation and treatment of patients.

ROBOGait: A Mobile Robotic Platform for Human Gait Analysis in Clinical Environments.

Mobile robotic platforms have made inroads in the rehabilitation area as gait assistance devices. They have rarely been used for human gait monitoring and analysis. The integration of mobile robots in this field offers the potential to develop multiple medical applications and achieve new discoveries. This study proposes the use of a mobile robotic platform based on depth cameras to perform the analysis of human gait in practical scenarios. The aim is to prove the validity of this robot and its applicability in clinical settings. The mechanical and software design of the system is presented, as well as the design of the controllers of the lane-keeping, person-following, and servoing systems. The accuracy of the system for the evaluation of joint kinematics and the main gait descriptors was validated by comparison with a Vicon-certified system. Some tests were performed in practical scenarios, where the effectiveness of the lane-keeping algorithm was evaluated. Clinical tests with patients with multiple sclerosis gave an initial impression of the applicability of the instrument in patients with abnormal walking patterns. The results demonstrate that the system can perform gait analysis with high accuracy. In the curved sections of the paths, the knee joint is affected by occlusion and the deviation of the person in the camera reference system. This issue was greatly improved by adjusting the servoing system and the following distance. The control strategy of this robot was specifically designed for the analysis of human gait from the frontal part of the participant, which allows one to capture the gait properly and represents one of the major contributions of this study in clinical practice.

Pressure Sensor Array With Low-Power Near-Sensor CMOS Chip for Human Gait Monitoring

Recent years have witnessed the emerging development of flexible electronic devices in applications, such as human gait monitoring. The automatic posture identification could be achieved by sensing the feet pressure. However, the sensor data processing chip usually consumes high power. In this letter, a wearable gait recognition system integrated with an ultrahigh-energy efficiency chip is proposed. A low-power human gait pressure monitoring and recognition solution that uses artificial intelligence near-sensor chips is reported. A large-scale flexible pressure sensor array (64 × 64 pixels) was fabricated. The gait posture was recognized dynamically with a CMOS chip embedded near the sensors. The system owns a recognition rate for three different gaits of 92% with the chip power consumption of 1.45 mW. This letter paves the way for future low-power human gait monitoring.

Ultralightweight and 3D Squeezable Graphene-Polydimethylsiloxane Composite Foams as Piezoresistive Sensors

The growing demand for flexible, ultrasensitive, squeezable, skin-mountable, and wearable sensors tailored to the requirements of personalized health-care monitoring has fueled the necessity to explore novel nanomaterial-polymer composite-based sensors. Herein, we report a sensitive, 3D squeezable graphene-polydimethylsiloxane (PDMS) foam-based piezoresistive sensor realized by infusing multilayered graphene nanoparticles into a sugar-scaffolded porous PDMS foam structure. Static and dynamic compressive strain testing of the resulting piezoresistive foam sensors revealed two linear response regions with an average gauge factor of 2.87–8.77 over a strain range of 0–50%. Furthermore, the dynamic stimulus–response revealed the ability of the sensors to effectively track dynamic pressure up to a frequency of 70 Hz. In addition, the sensors displayed a high stability over 36000 cycles of cyclic compressive loading and 100 cycles of complete human gait motion. The 3D sensing foams were applied to experimentally demonstrate accurate human gait monitoring through both simulated gait models and real-time gait characterization experiments. The real-time gait experiments conducted demonstrate that the information of the pressure profile obtained at three locations in the shoe sole could not only differentiate between different kinds of human gaits including walking and running but also identify possible fall conditions. This work also demonstrates the capability of the sensors to differentiate between foot anatomies, such as a flat foot (low central arch) and a medium arch foot, which is biomechanically more efficient. Furthermore, the sensors were able to sense various basic joint movement responses demonstrating their suitability for personalized health-care applications.

Deep Learning for Monitoring of Human Gait: A Review

The essential human gait parameters are briefly reviewed, followed by a detailed review of the state of the art in deep learning for the human gait analysis. The modalities for capturing the gait data are grouped according to the sensing technology: video sequences, wearable sensors, and floor sensors, as well as the publicly available datasets. The established artificial neural network architectures for deep learning are reviewed for each group, and their performance are compared with particular emphasis on the spatiotemporal character of gait data and the motivation for multi-sensor, multi-modality fusion. It is shown that by most of the essential metrics, deep learning convolutional neural networks typically outperform shallow learning models. In the light of the discussed character of gait data, this is attributed to the possibility to extract the gait features automatically in deep learning as opposed to the shallow learning from the handcrafted gait features.

Implementation of wireless MEMS sensor network for detection of gait events

MEMS based wireless sensor network (WSN) for real-time human health monitoring is promising in home-based rehabilitation. It requires effective integration of a number of MEMS sensors and their placement on the human body to create a wireless body area network for continuous and timely monitoring of various biophysical parameters. This study attempts to develop an XBee-based WSN for real-time monitoring of human gait. Traditionally, the optical motion systems were used for gait analysis but they suffer from certain limitations such as the development of the complex algorithm and constrains in the work space. In comparison to the optical system, the attractive benefits of MEMS-based sensor systems are small size and low cost. Magnetic field angular rate and gravity sensor system can perform a complete analysis of the human gait. The sensor modules were developed using the inertial sensors mainly accelerometer and gyro sensor. LabVIEW software is used for data acquisition from the body sensor nodes and gait analysis. Biometrics Lab System is used as the standard system for calibration of data obtained from sensors. The joint angle range of motion was calculated using both the systems. The advantage of the proposed system is that it facilitates wireless transmission of gait parameters (joint angle measurement) for easy monitoring of human gait in various rehabilitation programmes.

Piezoresistive nanocomposite films for foot strike data monitoring

It is important to understand the human gait especially for the patients under rehabilitation or in old age. Foot strike data is considered as the most important parameter to analyse gait. Piezoresponsive sensors are mostly used for this purpose. In this study, carbon nanofiber (CNF) based thermoplastic polyurethane (TPU) piezoresistive, nanocomposite films (15%, 20% and 25% w/w) were prepared, characterized and tested to study their suitability for such applications. Scanning electron microscope (SEM) studies on the cross section of nanocomposite films revealed that the CNF is well connected inside the TPU matrix. This well connected conducting network resulted in low bulk resistivity (98 ± 3.2 Ω-cm) and impedance (∼250 Ω). The compressive test results indicate more than threefold increase in strength due to CNF reinforcement. All the films showed excellent thickness recovery under cyclic loading while, 25% (w/w) CNF dispersed nanocomposite films’ showed more than 99% recovery after 100 cycles of cyclic compression. The samples also showed good dynamic mechanical stability from 1 to 10 Hz at body temperature (37 °C). The change in resistance under cyclic compression up to 70% strain was found to be linear with high correlation (R2 = 0.99). The gauge factor (up to 1.40 ± 0.03) shows little variation at different strain regions. A cyclic load setup was designed and fabricated to mimic the cyclic foot strike pattern of human gait. The nanocomposite films were tested at three different cyclic strain levels using the setup and the responses were monitored for 2 h using cathode ray oscilloscope (CRO). It has been observed that with the increase in CNF concentrations, the change in voltage goes up to 911.8 mV. The consistency of voltage response under cyclic compressive loading demonstrated in this work shows tremendous potential for these nanocomposite films to be successfully used for piezoresponsive applications of human gait monitoring.

Micro-Doppler Estimation and Analysis of Slow Moving Objects in Forward Scattering Radar System

Micro-Doppler signature can convey information of detected targets and has been used for target recognition in many Radar systems. Nevertheless, micro-Doppler for the specific Forward Scattering Radar (FSR) system has yet to be analyzed and investigated in detail; consequently, information carried by the micro-Doppler in FSR is not fully understood. This paper demonstrates the feasibility and effectiveness of FSR in detecting and extracting micro-Doppler signature generated from a target’s micro-motions. Comprehensive theoretical analyses and simulation results followed by experimental investigations into the feasibility of using the FSR for detecting micro-Doppler signatures are presented in this paper. The obtained results verified that the FSR system is capable of detecting micro-Doppler signature of a swinging pendulum placed on a moving trolley and discriminating different swinging speeds. Furthermore, human movement and micro-Doppler from hand motions can be detected and monitored by using the FSR system which resembles a potential application for human gait monitoring and classification.

A novel remote sensing technique for recognizing human gait based on the measurement of induced electrostatic current

In this paper, we propose an effective non-contact method for the monitoring of human gait including stepping, walking and running by measuring the induced electrostatic signals. Based on the kinematics principles and theories of electrodynamics in the view of capacitance variation, we analyze the characteristics of induced electrostatic current on electrode generated by human body motion without the need to attach electrode on human body to deduce the detection equations of human gait. The experimental results of human body motion recognition match the theoretical analysis well, and the obvious biped motion feature of induced electrostatic signal waveform proves that adopting this method in human body monitoring applications benefits in the low false-alarm rate, which verifies our expectation that the adoption of non-contact electrostatic detection technique will open up a new area of remote biometric measurements which can be applied in the field of human machine interface, health care and security.