Body Motion Data Research Articles

Research on Motion Capture System of Equestrians Based on Nine-Axis Inertial Sensor

The mechanical, optical and acoustic modes of traditional motion capture and analysis are bulky, expensive, and extremely complex. Micro inertial sensing action capture system is economical, convenient and is not affected by the location and the weather factors and other advantages of this study will be based on inertial sensing technology and human science combined with human abstracts and 17 points, 17 points through the acquisition of human body movement data, and then will be collected the data through WiFi sent to PC, and then the collected data on the PC, can get the attitude data driven model of the human body movement, so as to control the movement of the virtual model performers show the same motion capture system composition. The system realizes the real-time acquisition and analysis of human motion, and makes the training more scientific and efficient.

Security Analysis of the PHOTON Lightweight Cryptosystem in the Wireless Body Area Network

With the advancement and deployment of wireless communication techniques, wireless body area network (WBAN) has emerged as a promising approach for e-healthcare that collects the data of vital body parameters and movements for sensing and communicating wearable or implantable healthful related information. In order to avoid any possible rancorous attacks and resource abuse, employing lightweight ciphers is most effective to implement encryption, decryption, message authentication and digital signature for security of WBAN. As a typical lightweight cryptosystem with an extended sponge function framework, the PHOTON family is flexible to provide security for the RFID and other highly-constrained devices. In this paper, we propose a differential fault analysis to break three flavors of the PHOTON family successfully. The mathematical analysis and simulating experimental results show that 33, 69 and 86 random faults in average are required to recover each message input for PHOTON-80 /20/16, PHOTON-160/36/36 and PHOTON-224/32/32, respectively. It is the first result of breaking PHOTON with the differential fault analysis. It provides a new reference for the security analysis of the same structure of the lightweight hash functions in the WBAN.

Aplicaciones tecnológicas en análisis de movimiento: diagnóstico y seguimiento para procesos fisioterapéuticos

El presente trabajo muestra las perspectivas de aplicación y uso de la tecnología de análisis de movimiento para el diagnóstico y seguimiento en procesos terapéuticos, mediante el análisis de parámetros de movimiento, apoyado en la tecnología de BTS SMART-E. Esta tecnología permite el análisis de la cinemática de los movimientos corporales y registra datos e imágenes en tres dimensiones (3D), para permitir en análisis y la valoración del movimiento en los planos frontal, sagital y trasversal. El objetivo de este estudio es mostrar las posibles aplicaciones y el uso de esta tecnología. Se presentan los modelos para tres estudios de aplicación realizados: balance corporal, comportamiento de mano luego de una enfermedad cerebro vascular (ECV) y análisis de inestabilidad en columna lumbar. Estos modelos muestran la posibilidad de medir el comportamiento de uno o varios ángulos, en cuanto al desplazamiento, la velocidad y la aceleración. Este tipo de tecnología permite objetivar la evaluación y garantiza la reproducción de las pruebas en diferentes escenarios.

Validation of a New System Using Tracheal Body Sound and Movement Data for Automated Apnea-Hypopnea Index Estimation.

The current gold standard for assessment of obstructive sleep apnea is the in-laboratory polysomnography. This approach has high costs and inconveniences the patient, whereas alternative ambulatory systems are limited by reduced diagnostic abilities (type 4 monitors, 1 or 2 channels) or extensive setup (type 3 monitors, at least 4 channels). The current study therefore aims to validate a simplified automated type 4 monitoring system using tracheal body sound and movement data. Data from 60 subjects were recorded at the University Hospital Ulm. All subjects have been regular patients referred to the sleep center with suspicion of sleep-related breathing disorders. Four recordings were excluded because of faulty data. The study was of prospective design. Subjects underwent a full-night screening using diagnostic in-laboratory polysomnography and the new monitoring system concurrently. The apnea-hypopnea index (AHI) was scored blindly by a medical technician using in-laboratory polysomnography (AHIPSG). A unique algorithm was developed to estimate the apneahypopnea index (AHIest) using the new sleep monitor. AHIest strongly correlates with AHIPSG (r2 = .9871). A mean ± 1.96 standard deviation difference between AHIest and AHIPSG of 1.2 ± 5.14 was achieved. In terms of classifying subjects into groups of mild, moderate, and severe sleep apnea, the evaluated new sleep monitor shows a strong correlation with the results obtained by polysomnography (Cohen kappa > 0.81). These results outperform previously introduced similar approaches. The proposed sleep monitor accurately estimates AHI and diagnoses sleep apnea and its severity. This minimalistic approach may address the need for a simple yet reliable diagnosis of sleep apnea in an ambulatory setting. Trial name: Validation of a new method for ambulant diagnosis of sleep related breathing disorders using body sound; URL: https://drks-neu.uniklinik-freiburg.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00011195; Identifier: DRKS00011195.

Interjoint coordination of the lower extremities in short-track speed skating.

In short-track speed skating, the three-dimensional kinematics of the lower extremities during the whole skating cycle have not been studied. Kinematic parameters of the lower extremities during skating are presented as joint angles versus time. However, the angle-time presentation is not sufficient to describe the relationship between multi-joint movement patterns. Thus, angle-angle presentations were developed and used to describe interjoint coordination in sport activities. In this study, 15 professional male skaters' full body motion data were recorded using a wearable motion capture system during short-track speed skating. We investigated the three-dimensional kinematics of the lower extremities and then established the interjoint coordination between hip-knee and knee-ankle for both legs during the whole skating cycle. The results demonstrate the relationship between multi-joint movements during different phases of short-track speed skating. This study provides fundamentals of the movement mechanism of the lower extremities that can be integrated with physiotherapy to improve skating posture and prevent injuries from repetitive stress since physiological characteristics play an important role in skating performance.

Jumping in the Night: An Investigation of the Leaping Activity of the Western Tarsier (Cephalopachus bancanus borneanus) Using Accelerometers

Accelerometers enable scientists to quantify the activity of free-living animals whose direct observation is difficult or demanding due to their elusive nature or nocturnal habits. However, the deployment of accelerometers on small-bodied animals and, in particular, on primates has been little explored. Here we show the first application of accelerometers on the western tarsier (Cephalopachus bancanus borneanus), a nocturnal, small-bodied primate endemic to the forests of Borneo. The fieldwork was carried out in the Lower Kinabatangan Wildlife Sanctuary, Sabah, Malaysian Borneo. We provide guidelines for the deployment of accelerometers on tarsiers that might also be applied to other primate species. Our collected data on 2 females show levels of leaping activity comparable to those previously described using direct observation of wild or captive individuals. The 2 females showed different patterns of leaping activity, which calls for work to explore individual differences further. Our work demonstrates that accelerometers can be deployed on small primates to acquire body motion data that would otherwise be demanding to collect using classic field observations. Future work will be focused on using accelerometer data to discriminate in more detail the different behaviours tarsiers can display and to address the causes and consequences of individual variations in activity.

Musical Instruments, Body Movement, Space, and Motion Data: Music as an Emergent Multimodal Choreography

Musical Instruments, Body Movement, Space, and Motion Data: Music as an Emergent Multimodal Choreography

Lower Limb Muscle Fatigue Perturbs Gait Balance during Dual-Task Walking

Walking and simultaneously performing an attention demanding task may occur concurrently with increasing muscle fatigue toward the end of some daily activities or job performance. Although previous research have demonstrated independent effect of these factors, their interaction are rarely taken into consideration. PURPOSE: To examine changes in gait balance and working memory performance in healthy college-age adults after lower extremity muscle fatigue. METHODS: Ten healthy adults (5 females, 20.6±1.0 yrs) performed the following three tasks before and after a muscle fatigue protocol: 1) Walking with a self-selected pace, 2) Sitting and performing a 3-back test, in which participants listened a series of digits over a loudspeaker and were instructed to verbally respond “yes” whenever a digit is heard that is the same as presented three positions back in the series, and 3) Walking and performing a 3-back test simultaneously. Sit-to-stand task at a pace of 0.5 Hz was performed to induce muscle fatigue. Maximal voluntary isometric strength of knee extensors was assessed using Biodex before and after the fatigue protocol and at the end of study. Whole body motion data were collected from a set of 29 retro-reflective markers placed on bony landmarks with a 10-camera motion system. Gait balance control was examined using the total medial-lateral CoM displacement (M-LCoM). Two-way ANOVA with repeated measures were used to detect differences between single and dual-task conditions. RESULTS: An average of 21% knee extensor strength reduction was observed immediately after the completion of fatigue protocol, and it was recovered to approximately 10% by the end of study. In both gait conditions, M-LCoM was found to increase significantly after fatigue (3.1±0.2 vs. 3.7±0.3 cm, p = .01). Accuracy of the 3-back test was not significantly affected by the fatigue or gait condition. CONCLUSIONS: Our preliminary findings indicated that gait balance control, as measured by the CoM sway, during a dual-task gait task might be more sensitively affected by the acute muscle fatigue induced in the current study.

Application of Deep Learning to Develop a Safety Confirmation System for the Elderly in a Nursing Home

[abstFig src='/00290002/07.jpg' width='210' text='The motion detection system' ] Fall accident prevention is one of the most important issues in elderly care settings. To prevent an accident, it is necessary to notify caregivers if the elderly person is getting out of bed. We have previously developed a posture discrimination system based on body motions. Herein, we propose a discrimination method by using machine learning to improve the performance of the system. A purpose of this study is to evaluate the proposed method. Elderly people in a nursing home were chosen as subjects in this study. We analyzed the body motion data during bed rest and bed exit of the subjects using the proposed method. These results suggest that it is effective.

Visual Data Exploration for Balance Quantification in Real-Time During Exergaming.

Unintentional injuries are among the ten leading causes of death in older adults; falls cause 60% of these deaths. Despite their effectiveness to improve balance and reduce the risk of falls, balance training programs have several drawbacks in practice, such as lack of engaging elements, boring exercises, and the effort and cost of travelling, ultimately resulting in low adherence. Exergames, that is, digital games controlled by body movements, have been proposed as an alternative to improve balance. One of the main challenges for exergames is to automatically quantify balance during game-play in order to adapt the game difficulty according to the skills of the player. Here we perform a multidimensional exploratory data analysis, using visualization techniques, to find useful measures for quantifying balance in real-time. First, we visualize exergaming data, derived from 400 force plate recordings of 40 participants from 20 to 79 years and 10 trials per participant, as heat maps and violin plots to get quick insight into the nature of the data. Second, we extract known and new features from the data, such as instantaneous speed, measures of dispersion, turbulence measures derived from speed, and curvature values. Finally, we analyze and visualize these features using several visualizations such as a heat map, overlapping violin plots, a parallel coordinate plot, a projection of the two first principal components, and a scatter plot matrix. Our visualizations and findings suggest that heat maps and violin plots can provide quick insight and directions for further data exploration. The most promising measures to quantify balance in real-time are speed, curvature and a turbulence measure, because these measures show age-related changes in balance performance. The next step is to apply the present techniques to data of whole body movements as recorded by devices such as Kinect.

Energy expenditure estimation in beta-blocker-medicated cardiac patients by combining heart rate and body movement data.

Accurate assessment of energy expenditure provides an opportunity to monitor physical activity during cardiac rehabilitation. However, the available assessment methods, based on the combination of heart rate (HR) and body movement data, are not applicable for patients using beta-blocker medication. Therefore, we developed an energy expenditure prediction model for beta-blocker-medicated cardiac rehabilitation patients. Sixteen male cardiac rehabilitation patients (age: 55.8 ± 7.3 years, weight: 93.1 ± 11.8 kg) underwent a physical activity protocol with 11 low- to moderate-intensity common daily life activities. Energy expenditure was assessed using a portable indirect calorimeter. HR and body movement data were recorded during the protocol using unobtrusive wearable devices. In addition, patients underwent a symptom-limited exercise test and resting metabolic rate assessment. Energy expenditure estimation models were developed using multivariate regression analyses based on HR and body movement data and/or patient characteristics. In addition, a HR-flex model was developed. The model combining HR and body movement data and patient characteristics showed the highest correlation and lowest error (r2 = 0.84, root mean squared error = 0.834 kcal/minute) with total energy expenditure. The method based on individual calibration data (HR-flex) showed lower accuracy (i2 = 0.83, root mean squared error = 0.992 kcal/minute). Our results show that combining HR and body movement data improves the accuracy of energy expenditure prediction models in cardiac patients, similar to methods that have been developed for healthy subjects. The proposed methodology does not require individual calibration and is based on the data that are available in clinical practice.

Exploration Analysis Based on Improved BP Neural Network in Human Body Movement

This paper fist gives common definition of human body movement data model and establishes human body movement model according to kinemaics of human body, and then adopts improved BP neural network to analyze and predict human body movement. Introducing genetic algorithm into BP neural network can better overcome problems caused by random preliminary value of BP neural network during the process of determining network structure, such as network vibration and it is easy for network to fall into local solution. In addition, generalization ability of neural network can be effectively improved. It can be obtained from analyzing and comparing simulation experiments that the accuracy of BP neural network algorithm in analyze and predict human body movement is slightly improved.

Learning Robot Manipulation Tasks With Task-Parameterized Semitied Hidden Semi-Markov Model

In this letter, we investigate the semitied Gaussian mixture models for robust learning and adaptation of robot manipulation tasks. We make use of the spatial and temporal correlation in the data by tying the covariance matrices of the mixture model with common synergistic directions/basis vectors, instead of estimating full covariance matrices for each cluster in the mixture. This allows the reuse of the discovered synergies in different parts of the task having similar coordination patterns. We extend the approach to task-parameterized and hidden semi-Markov models for autonomous adaptation to changing environmental situations. The planned movement sequence from the model is smoothly followed with a finite horizon linear quadratic tracking controller. Experiments to encode whole body motion data in simulation, followed by valve opening and pick-and-place via obstacle avoidance tasks with the Baxter robot, show improvement over standard Gaussian mixture models with much less parameters and better generalization ability.

Enhanced secure sensor association and key management in wireless body area networks

Body area networks (BANs) have emerged as an enabling technique for e-healthcare systems, which can be used to continuously and remotely monitor patients' health. In BANs, the data of a patient's vital body functions and movements can be collected by small wearable or implantable sensors and sent using short-range wireless communication techniques. Due to the shared wireless medium between the sensors in BANs, it may be possible to have malicious attacks on e-healthcare systems. The security and privacy issues of BANs are becoming more and more important. To provide secure and correct association of a group of sensors with a patient and satisfy the requirements of data confidentiality and integrity in BANs, we propose a novel enhanced secure sensor association and key management protocol based on elliptic curve cryptography and hash chains. The authentication procedure and group key generation are very simple and efficient. Therefore, our protocol can be easily implemented in the power and resource constrained sensor nodes in BANs. From a comparison of results, furthermore, we can conclude that the proposed protocol dramatically reduces the computation and communication cost for the authentication and key derivation compared with previous protocols. We believe that our protocol is attractive in the application of BANs.

Application of sociometer badges in simulated health environments

Workload experienced by health care workers continues to be a challenge to define and quantify. The purpose of the current study is to present descriptive data on the application of sociometers in an emergency care environment during simulated hand-off scenarios and discuss their potential and limitations for quantifying individual’s workload in health care settings. Sociometer devices, worn by nine actors, collected body movement, interactions, and speech data during four simulated hand-off scenarios in the emergency department wards. Results found that sociometers distinguished body movement differences between sitting, standing, lying, and walking individuals. Interactions quantified by the devices were limited by obstructions, distance, and angles between the sociometer devices. The data collected by these devices show promise in providing human factors researchers a tool for quantifying the dynamic exposures experienced by health care workers over time.

Lumbar joint torque estimation based on simplified motion measurement using multiple inertial sensors.

We estimate lumbar torque based on motion measurement using only three inertial sensors. First, human motion is measured by a 6-axis motion tracking device that combines a 3-axis accelerometer and a 3-axis gyroscope placed on the shank, thigh, and back. Next, the lumbar joint torque during the motion is estimated by kinematic musculoskeletal simulation. The conventional method for estimating joint torque uses full body motion data measured by an optical motion capture system. However, in this research, joint torque is estimated by using only three link angles of the body, thigh, and shank. The utility of our method was verified by experiments. We measured motion of bendung knee and waist simultaneously. As the result, we were able to estimate the lumbar joint torque from measured motion.

Quaternion-valued echo state networks.

Quaternion-valued echo state networks (QESNs) are introduced to cater for 3-D and 4-D processes, such as those observed in the context of renewable energy (3-D wind modeling) and human centered computing (3-D inertial body sensors). The introduction of QESNs is made possible by the recent emergence of quaternion nonlinear activation functions with local analytic properties, required by nonlinear gradient descent training algorithms. To make QENSs second-order optimal for the generality of quaternion signals (both circular and noncircular), we employ augmented quaternion statistics to introduce widely linear QESNs. To that end, the standard widely linear model is modified so as to suit the properties of dynamical reservoir, typically realized by recurrent neural networks. This allows for a full exploitation of second-order information in the data, contained both in the covariance and pseudocovariances, and a rigorous account of second-order noncircularity (improperness), and the corresponding power mismatch and coupling between the data components. Simulations in the prediction setting on both benchmark circular and noncircular signals and on noncircular real-world 3-D body motion data support the analysis.

Correlated space formation for human whole-body motion primitives and descriptive word labels

The motion capture technology has been improved, and widely used for motion analysis and synthesis in various fields, such as robotics, animation, rehabilitation, and sports engineering. A massive amount of captured human data has already been collected. These prerecorded motion data should be reused in order to make the motion analysis and synthesis more efficient. The retrieval of a specified motion data is a fundamental technique for the reuse. Imitation learning frameworks have been developed in robotics, where motion primitive data is encoded into parameters in stochastic models or dynamical systems. We have also been making research on encoding motion primitive data into Hidden Markov Models, which are referred to as “motion symbol”, and aiming at integrating the motion symbols with language. The relations between motions and words in natural language will be versatile and powerful to provide a useful interface for reusing motion data. In this paper, we construct a space of motion symbols for human whole body movements and a space of word labels assigned to those movements. Through canonical correlation analysis, these spaces are reconstructed such that a strong correlation is formed between movements and word labels. These spaces lead to a method for searching for movement data from a query of word labels. We tested our proposed approach on captured human whole body motion data, and its validity was demonstrated. Our approach serves as a fundamental technique for extracting the necessary movements from a database and reusing them.

1P1-W05 慣性センサを用いた動作計測に基づくアシスト器具の補助効果シミュレーション

This research aims to estimate effect of assist device in real work field. First, Human motion is measured by 6-axis motion tracking devise that combines a 3-axis accelerometer and a 3-axis gyroscope placed on shank, thigh and back. Next, lumbar joint torque and assisting effect of Smart Suit Lite in the motion are estimated by interactive musculoskeletal simulation. Conventional method to estimate joint torque is using full body motion data measured by optical motion capturing system. But in this research, joint torque can be estimated by only 3 angles of links which is body, thigh and lower leg. Usability of proposal method is verified by the field experiment. As the result of the experiment, workload was evaluated and assistive effect of Smart Suit Lite was estimated by measured motion.

Development of Motion Navigator II Enabling Integrated Motion Display of Whole Body with Fingers

In this study, we build a new system “Motion Navigator II”, which displays finger motion integrated with whole body motion with bone CG animation by AR. The function to display whole body motion is inherited from an existing motion skill learning support system “Motion Navigator”. The existing Motion Navigator can’t display fingers’ motion but shows limited variation of motion in precedent research. Therefore we acquired finger motion data using data gloves and improved Motion Navigator integrating the finger motion data with whole body motion data. We verified learning effect of the experimental group using Motion Navigator II in comparison with the control group using conventional video.