Artificial Intelligence-Enabled Caregiving Walking Stick Powered by Ultra-Low-Frequency Human Motion

ACS Nano ◽  
2021 ◽  
Author(s):  
Xinge Guo ◽  
Tianyiyi He ◽  
Zixuan Zhang ◽  
Anxin Luo ◽  
Fei Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Low Frequency ◽  
Human Motion ◽  
Walking Stick

Electrostatic micro power generation from low-frequency vibration such as human motion

2009 ◽  
Vol 19 (9) ◽  
pp. 094002 ◽  
Author(s):  
Y Naruse ◽  
N Matsubara ◽  
K Mabuchi ◽  
M Izumi ◽  
S Suzuki
Keyword(s):  
Power Generation ◽  
Low Frequency ◽  
Human Motion ◽  
Low Frequency Vibration ◽  
Micro Power

scholarly journals Portable Mobile Gait Monitor System Based on Triboelectric Nanogenerator for Monitoring Gait and Powering Electronics

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4996
Author(s):  
Yupeng Mao ◽  
Yongsheng Zhu ◽  
Tianming Zhao ◽  
Changjun Jia ◽  
Xiao Wang ◽  
...  
Keyword(s):  
Electronic Device ◽  
Low Frequency ◽  
Aluminum Foil ◽  
Development Trend ◽  
Human Motion ◽  
Triboelectric Nanogenerator ◽  
Portable Devices ◽  
Voltage Output ◽  
Self Powered ◽  
Triboelectric Effect

A self-powered portable triboelectric nanogenerator (TENG) is used to collect biomechanical energy and monitor the human motion, which is the new development trend in portable devices. We have developed a self-powered portable triboelectric nanogenerator, which is used in human motion energy collection and monitoring mobile gait and stability capability. The materials involved are common PTFE and aluminum foil, acting as a frictional layer, which can output electrical signals based on the triboelectric effect. Moreover, 3D printing technology is used to build the optimized structure of the nanogenerator, which has significantly improved its performance. TENG is conveniently integrated with commercial sport shoes, monitoring the gait and stability of multiple human motions, being strategically placed at the immediate point of motion during the respective process. The presented equipment uses a low-frequency stabilized voltage output system to provide power for the wearable miniature electronic device, while stabilizing the voltage output, in order to effectively prevent voltage overload. The interdisciplinary research has provided more application prospects for nanogenerators regarding self-powered module device integration.

Artificial Intelligence in Psychomotor Learning: Modeling Human Motion from Inertial Sensor Data

2019 ◽  
Vol 28 (04) ◽  
pp. 1940006 ◽  
Author(s):  
Olga C. Santos
Keyword(s):  
Artificial Intelligence ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Musical Instrument ◽  
Human Motion ◽  
Learning Performance ◽  
Sensor Data ◽  
Psychomotor Skills ◽  
Motion Modeling ◽  
Psychomotor Learning

Recent trends in educational technology focus on designing systems that can support students while learning complex psychomotor skills, such as those required when practicing sports and martial arts, dancing or playing a musical instrument. In this context, artificial intelligence can be key to personalize the development of these psychomotor skills by enabling the provision of effective feedback when the instructor is not present, or scaling up to a larger pool of students the feedback that an instructor would typically provide one-on-one. This paper presents the modeling of human motion gathered with inertial sensors aimed to offer a personalized support to students when learning complex psychomotor skills. In particular, when comparing learner data with those of an expert during the psychomotor learning process, artificial intelligence algorithms can allow to: (i) recognize specific motion learning units and (ii) assess learning performance in a motion unit. However, it seems that this field is still emerging, since when reviewed systematically, search results hardly included the motion modeling with artificial intelligence techniques of complex human activities measured with inertial sensors.

scholarly journals An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion

2021 ◽  
Vol 11 (5) ◽  
pp. 2008
Author(s):  
Mahesh Edla ◽  
Yee Yan Lim ◽  
Ricardo Vasquez Padilla ◽  
Mikio Deguchi
Keyword(s):  
Output Power ◽  
Real Life ◽  
Low Frequency ◽  
Human Motion ◽  
Storage Device ◽  
Small Scale ◽  
Sinusoidal Vibration ◽  
Rectifier Circuit ◽  
Piezoelectric Energy ◽  
Bridge Rectifier

Harvesting energy from human motion for powering small scale electronic devices is attracting research interest in recent years. A piezoelectric device (PD) is capable of harvesting energy from mechanical motions, in the form of alternating current (AC) voltage. The AC voltage generated is of low frequency and is often unstable due to the nature of human motion, which renders it unsuitable for charging storage device. Thus, an electronic circuit such as a full bridge rectifier (FBR) is required for direct current (DC) conversion. However, due to forward voltage loss across the diodes, the rectified voltage and output power are low and unstable. In addition, the suitability of existing rectifier circuits in converting AC voltage generated by PD as a result of low frequency human motion induced non-sinusoidal vibration is unknown. In this paper, an improved H-Bridge rectifier circuit is proposed to increase and to stabilise the output voltage. To study the effectiveness of the proposed circuit for human motion application, a series of experimental tests were conducted. Firstly, the performance of the H-Bridge rectifier circuit was studied using a PD attached to a cantilever beam subject to low frequency excitations using a mechanical shaker. Real-life testing was then conducted with the source of excitation changed to a human performing continuous cycling and walking motions at a different speed. Results show that the H-Bridge circuit prominently increases the rectified voltage and output power, while stabilises the voltage when compared to the conventional FBR circuit. This study shows that the proposed circuit is potentially suitable for PEH from human motion.

scholarly journals Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial Intelligence

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhao Zhang ◽  
Wang Li ◽  
Yuyang Zhang
Keyword(s):  
Artificial Intelligence ◽  
Feature Extraction ◽  
Classification Problem ◽  
Human Motion ◽  
Small Samples ◽  
Support Vector ◽  
Convolutional Network ◽  
Multilabel Classification ◽  
Practical Applications ◽  
Automatic Construction

In this paper, we study the automatic construction and extraction of feature variables of sports moments and construct the extraction of the specific variables by artificial intelligence. In this paper, support vector machines, which have better performance in the case of small samples, are selected as classifiers, and multiclass classifiers are constructed in a one-to-one manner to achieve the classification and recognition of human sports postures. The classifier for a single decomposed action is constructed to transform the automatic description problem of free gymnastic movements into a multilabel classification problem. With the increase in the depth of the feature extraction network, the experimental effect is enhanced; however, the two-dimensional convolutional neural network loses temporal information when extracting features, so the three-dimensional convolutional network is used in this paper for spatial-temporal feature extraction of the video. The extracted features are binary classified several times to achieve the goal of multilabel classification. To form a comparison experiment, the results of the classification are randomly combined into a sentence and compared with the results of the automatic description method to verify the effectiveness of the method. The multiclass classifier constructed in this paper is used for human motion pose classification and recognition tests, and the experimental results show that the human motion pose recognition algorithm based on multifeature fusion can effectively improve the recognition accuracy and perform well in practical applications.

scholarly journals Physical Education Teaching in Colleges and Universities Assisted by Virtual Reality Technology Based on Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yuqing Wang
Keyword(s):  
Artificial Intelligence ◽  
Virtual Reality ◽  
Physical Education ◽  
Three Dimensional ◽  
Colleges And Universities ◽  
Human Motion ◽  
Virtual Human ◽  
Virtual Reality Technology ◽  
Motion Data ◽  
Human Animation

Virtual reality technology has promoted the reform of education. This research mainly discusses college physical education teaching assisted by artificial intelligence-based virtual reality technology. According to the position change of the virtual human’s center of gravity, the spline keyframe interpolation method is used for interpolation, and the model pose obtained in each frame is rendered to obtain the virtual human’s animation. After synthesizing a virtual human animation with three-dimensional human motion data, the animation can have functions such as video storage, fast playback, slow playback, and freeze. At the same time, the system can also display and play the virtual human animation and the video shot by the camera on the same screen, in order to make an intuitive comparison of the athletes’ movements. Coaches can edit by hand or shoot the sports of outstanding domestic and foreign athletes on the spot and then use VC++6.0 as a development tool to analyze and get the simulation video of the 3D virtual human body. The virtual human animation technology in the motion analysis system is to relocate the three-dimensional motion data extracted from the video captured by the camera to the three-dimensional virtual human model we have established, and the three-dimensional virtual human will then simulate the technical actions of the athletes, which indirectly reflects that the three-dimensional movement information of the athletes enables coaches and athletes to observe the athletes’ technical movements in a three-dimensional space in real time, repeatedly, and from multiple angles so that the coach can accurately guide the athletes’ technical movements. Finally, a neural network based on artificial intelligence technology is used to evaluate the teaching effect. In the comparative experiment, 35% of the people in the virtual teaching experiment group were excellent, while the control group had only 10% in this excellent range (90–100). This research contributes to the smooth progress of VR technology teaching in colleges and universities.

scholarly journals Intelligent Sports Training System Based on Artificial Intelligence and Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chunguang Li ◽  
Jianbiao Cui
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Human Body ◽  
Joint Angle ◽  
Rapid Development ◽  
Human Motion ◽  
Training System ◽  
Human Motion Analysis ◽  
Sports Training ◽  
Organizational System

All activities in training fields are for the improvement of athletes’ competitive abilities. A sports training system is an organizational system to achieve common goals. Competitive ability is one of the main manifestations of the evolution of the training system. With the rapid development of computer technology, people have begun to combine virtual reality and other technologies to achieve scientific sports-assisted training to eliminate traditional sports training that relied purely on experience. Pose estimation obtains the position, angle, and additional information about the human body in the image in a two-dimensional plane or three-dimensional space by establishing the mapping relationship between the human body features and the human body posture. This article demonstrates a golf-assisted training system to realize the transformation from an experience-based sports training method to a human motion analysis method, using artificial intelligence and big data. The swing posture parameters of the trainer and the coach are obtained using the posture estimation of a human body. Based on this information, an auxiliary training system is built. The two parameters of the joint angle trajectory and the posture similarity are used as auxiliary indicators to compare the trainers. The joint angle trajectory is analyzed, and the coach is guided based on the similarity of the posture.

scholarly journals A Survey of Human Gait-Based Artificial Intelligence Applications

2022 ◽  
Vol 8 ◽  
Author(s):  
Elsa J. Harris ◽  
I-Hung Khoo ◽  
Emel Demircan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Gait Analysis ◽  
Human Motion ◽  
Biomechanical Analysis ◽  
Machine Learning Techniques ◽  
Human Gait ◽  
Pose Tracking ◽  
Learning Techniques ◽  
Applications Of Machine Learning

We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.

Vehicle motion and motion sickness in pigs

1998 ◽  
Vol 66 (1) ◽  
pp. 239-245 ◽  
Author(s):  
J. M. Randall ◽  
R. H. Bradshaw
Keyword(s):  
Motion Sickness ◽  
Predictive Models ◽  
Low Frequency ◽  
Vertical Axis ◽  
Human Motion ◽  
Human Model ◽  
Road Vehicles ◽  
Low Frequencies ◽  
Vehicle Motion ◽  
Motion Characteristics

AbstractLow frequency oscillatory motion (0·05 to 0·5 Hz) experienced in ships and road vehicles is known to cause motion sickness in humans and some predictive models are available. There have been very few studies of the incidence of motion sickness in pigs and none which has attempted to identify the frequencies of motion of transporters which are likely to be implicated. In this study, the vibration and motion characteristics of a commercial pig transporter were measured while seven individually penned 40-kg pigs were transported for short (100 min) journeys and 80-kg pigs penned in groups of 12 or 13 were transported for longer (4·5 h) journeys. Direct behavioural observations were made of individual pigs for symptoms of travel sickness (sniffing, foaming at the mouth, chomping, and retching or vomiting). A comparison was then made between the incidence of travel sickness in pigs and that expected in humans given the measured vehicle vibration characteristics. The low frequencies of motion measured on the transporter (0·01 to 0·2 Hz) were well within the range implicated in human motion sickness with considerable power in the longitudinal and lateral axes but little in the vertical axis. On both short and long journeys pigs exhibited symptoms of travel sickness. The likely incidence of travel sickness on the short journeys predicted by the human model was 24 to 31% which corresponds to approximately two of the seven 40-kg pigs becoming travel sick. The numbers observed were generally lower than this although the same pigs were transported twice each day for 2 days and this may have therefore reflected the effects of habituation. The incidence of travel sickness on the long journeys predicted by the human model was 34%. During these journeys which involved four groups of 80-kg pigs which were not repeatedly transported, 26% of pigs vomited or retched (13 out of 50) while 50% showed advanced symptoms of foaming and chomping. These results are not inconsistent with the human model which should form the basis offurther research.

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