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Background/Aims The effect of peroral endoscopic myotomy (POEM) on esophageal body movement in achalasia is poorly understood. This study aims to evaluate morphological changes in esophageal body movement after POEM in type III achalasia by analyzing intraluminal ultrasound (US) images in comparison to type I and II achalasia. Methods Intraluminal US images and impedance values of the distal esophagus from 47 achalasia patients who underwent POEM or pneumatic dilatation (PD) (30 patients in the POEM group and 17 patients in the PD group) with pre- and post-procedural high-resolution impedance manometry and intraluminal US examinations were analyzed. The muscle thickness (MT), muscle cross-sectional area, lumen cross-sectional area (LCSA), contractility and distensibility indices, swallow-to-distension interval, and distension duration during each bolus transport were analyzed. Results The MT increased and LCSA decreased significantly (P < 0.001), but the contractility index was not improved after POEM or PD in type I achalasia. Baseline MT increased and LCSA decreased significantly after POEM and PD in type II achalasia (P < 0.001). In contrast, MT and the swallow-to-distension interval decreased and the distension LCSA/duration and contractility index increased after POEM in type III achalasia (P < 0.001). In contrast to type I and II achalasia, in type III achalasia, these effects were unique to the POEM group. Conclusions POEM decreased the esophageal LCSA by decreasing intrabolus pressure without improving contractility in type I and II achalasia. In contrast, POEM increased esophageal body distension and contractility and improved the inhibitory process during bolus transport in type III achalasia.

Most conventional vital-sign radars cannot easily detect human vital sign signals under nonperiodic body movement. This article presents a novel frequency-locked loop (FLL) radar that can track the phase changes that are caused by nonperiodic body movement. A gain/phase detector and a cross correlation function (CCF) in the FLL radar greatly reduce the second harmonic, the intermodulation terms between vital sign signals and nonperiodic body movement noise, enhancing both the signal-to-noise ratio and the accuracy of detection of the vital signs. Measurements indicated that the implemented 2.4-GHz FLL radar with a gain/phase detector and CCF can effectively detect the human respiration rate (RR) and heart rate (HR) under nonperiodic body movement. Using a chest belt as the measurement reference, the standard deviation (SD) of RR and HR measurements were 1.27 and 5.69 beats/min, respectively, when the seated subject under test (SUT) swung nonperiodically his upper body. When the SUT walked away from the radar naturally at 2.6 km/h, the implemented FLL radar measured RR and HR with SDs of 2.67 and 8.28 beats/min, respectively.

This article discusses drawing as a tool for thinking and understanding embodied spatial experience in an educational context within architecture and dance. In both disciplines, body, movement and space are important components of the creative process. Besides, they also share drawing as a tool for thinking and understanding. However, the types of drawings and drawing techniques used in both disciplines are very different. Based on a literature review, this article offers insights into the knowledge and shortcomings produced by discipline-specific drawings regarding body, movement and space, as well as how it impacts the creative process. Starting from the identified shortcomings, experimental setups in educational environments are used to investigate to what extent the sharing of drawing techniques between both disciplines enables new ways of thinking and understanding, allowing for different attitudes and awareness of the concept of embodied spatial experience. Overall, the article contributes to the discussion of drawing as a creative thinking tool in both disciplines involved.

In view of the problems of low precision, poor quality, and long time of gait feature recognition due to the influence of human body movement environment on the recognition process of the current gait feature recognition method of human body movement blurred image, a new method of gait feature recognition based on graph neural network (GNN) method is proposed. The gait features of human movement blurred images were extracted, and the fusion clustering recognition of the GNN algorithm was used to locate the gait features of human movement blurred images. The gait features of human body movement blurred images were located by the GNN method. According to the contour feature point info of the human body movement blurred image, the standard deviation of gait feature location of the human body movement blurred image was calculated, the gait feature of the blurred image of human body movement was reconstructed, and the gait recognition of the human body movement blurred image was achieved. The results show that the extraction of human movement is good, with high positioning confidence, good recognition quality, average recognition accuracy of 92%, and greatly shortened recognition time.

Radio frequency (RF) sensors such as radar are instrumental for continuous, contactless sensing of vital signs, especially heart rate (HR) and respiration rate (RR). However, decades of related research mainly focused on static subjects, because the motion artifacts from other body parts may easily overwhelm the weak reflections from vital signs. This paper marks a first step in enabling RF vital sign sensing under ambulant daily living conditions. Our solution is inspired by existing physiological research that revealed the correlation between vital signs and body movement. Specifically, we propose to combine direct RF sensing for static instances and indirect vital sign prediction based on movement power estimation. We design customized machine learning models to capture the sophisticated correlation between RF signal pattern, movement power, and vital signs. We further design an instant calibration and adaptive training scheme to enable cross-subjects generalization, without any explicit data labeling from unknown subjects. We prototype and evaluate the framework using a commodity radar sensor. Under a variety of moving conditions, our solution demonstrates an average estimation error of 5.57 bpm for HR and 3.32 bpm for RR across multiple subjects, which largely outperforms state-of-the-art systems.

Worldwide, 16.7 million people die each year due to cardiovascular disease. These statistics raise demand of devices like sensor-based pacemakers (PM) which are not just doing heart rate augmentation but also capable to transmit information via wireless link to on body sensor and support remote monitoring of such patients. As per the world health organization WHO reports there are more than 3 million functioning PMs and about 600,000 pacemakers are implanted each year in world. On an average, 70–80% of PMs are implanted in aged patients around 65 years or older. In addition to continuous monitoring of cardiovascular parameters, detection of physical movement of such patients may be helpful to assess their well-being. This paper has been formulated with an aim to highlight an approach which may be used to detect the physical movement of the patient using information signal received from implanted PM. The transmitted signal will experience a pathloss offered by wireless human body channel, which will affect the link quality parameters namely Signal to Noise Ratio (SNR) and Bit Error Rate (BER) and received signal strength indicator (RSSI). In the current work mathematical model has been formulated considering in body and on body channel propagation conditions and received power, received energy, pathloss, SNR, BER, bit rate, energy per bit and RSSI have been evaluated using IEEE802.15.6 channel models CM2 and CM3. Data set has been created and human body movement has been detected using Machine Learning (ML) techniques. Prediction accuracy of Multilayer Perceptron (MLP), k-Nearest Neighbours (kNN) and Random Forest have been compared. The analysis performed depicts that human body movement can be detected using different channel models and ML techniques such as MLP, kNN and Random Forest with an accuracy of 65.3%, 72.8% and 93.4% respectively. The critical comparison of the result indicates that the performance of Random Forest is better than MLP and kNN. This approach will be helpful in remote detection of human body movement of patients.

Research has investigated the use of non-invasive brain interventions, such as transcranial direct current stimulation (tDCS), to enhance motor learning and rehabilitation. Much research has shown that tDCS improves motor learning and that bilateral tDCS is more beneficial than unilateral tDCS in improving motor learning. However, past research has primarily utilised simple motor tasks in measuring motor skill learning. These are not ecologically reliable as whole-body movement is required for everyday activities. This study involved two experiments. Each experiment involved participants learning 12 Ballroom and Latin dance moves whilst undergoing tDCS. All participants underwent three sessions of tDCS, (unilateral, bilateral and sham), over three consecutive days. Participants in the first experiment (n=30) had stimulation to the primary motor cortex (PMC) and those in the second experiment (n=31) had stimulation to the dorsolateral prefrontal cortex (DLPFC). In each experiment, a baseline was taken before the training sessions and two outcome measures were taken; a day after the last training session and two weeks later. In each testing session participants’ dance ability was measured using a double-blind protocol. Our results showed that bilateral tDCS impaired performance in both experiments. Unilateral stimulation impaired performance in the first experiment, and did not significantly improve performance any better than the sham stimulation in the second experiment. These results suggest that task complexity plays a crucial role when tDCS procedures are used to modulate motor performance and highlights possible limitations of tDCS in practice.

In physical education class, the movement of the human body requires multiple joints to cooperate, and a multi-link system coupling is presented. In the teaching of physical education curriculum, the impact of the force received by students jumping up and down shows the characteristics of the non-linear system of physics and mathematics. Aiming at the movement process of jumping up and down, we established a joint mathematical equation model of the motion state of the human lower limb joints. We use a non-linear system to solve the mathematical model of the joint force coupling problem of the human body jumping up and down.

BACKGROUND The study of clusters of risk factors for elevated blood pressure (EBP) among adolescents from low- middle-income countries has received little research attention. The objective of the study was to identify clusters of EBP precursors (i.e., overweight, physical fitness, risk diet, and body movement) and to analyze its associations with EBP. METHODS This a cross-sectional study conducted in a sample of 965 secondary schools' students aged between 12 and 17 years-old from Colombia. Measurements included blood pressure, sociodemographic information, and four EBP precursors: Overweight, body movement, risk diet, and physical fitness. Cluster analysis was applied with the two-step method to identify the patterns of EBP precursors, and logistic regression models were used to analyze the association between clusters and EBP. RESULTS Findings indicate that EBP was present in 28.9% of the students, approximately 20% of adolescents had desirable profile (normal weight, normal-high physical fitness, low-risk diet, and middle-high body movement), and 16% of them had the four EBP precursors. The cluster solution revealed five clusters, formed according to the accumulation of EBP precursors, this is, characterized by having zero, one (low body movement), two (low body movement and high-risk diet), three (low body movement, high risk diet, and low fitness), or four (low body movement, high risk diet, low fitness, and overweight) EBP precursors. The cluster with four EBP precursors had a higher risk of having EBP. CONCLUSIONS According to these findings, it is necessary to implement programs that simultaneously address multiple health-related behaviors to promote health in adolescents.

The didactic possibilities of the body movement modeling by replacing it with the material points system, which connected among themselves by weightless viscous elastic rods, are discussed. It is shown that the modeling of the body by a system of particles bound by viscoelastic weightless rods, obeying the basic law of dynamics, really allows: 1) to obtain the computer animation, i.e. the visual image of moving body at successive moments of time; 2) to plot the change of coordinates, velocities and accelerations of any of its points over time; 3) to model the translational, rotational, flat and spherical motion of the body, the interaction of the body with the surface or other body, to study the swimming of bodies, precession of the gyroscope and other phenomena; 4) to activate the mental activity of students, increase their motivation to learn. The tasks are considered: about collision of viscoelastic body with solid surface; about destruction of beam with defect; about the rotating wheel falling on surface; about flight of the rubber ring; about collision of two bodies; about the swimming of body with attached load; about the flexible ruler falling on cylinder; about the chain falling on cylinder; about the disk rotating on the horizontal surface; about the tumbling disk; about the gyroscope precession. The paper contains 8 figures obtained as a result of computer modeling of the above phenomena. As result of the analysis of the solved problems, it is shown that the modeling of a viscoelastic body by the method of bound particles has great didactic possibilities. It allows: 1) to study translational, rotational motion, deformation of bodies, gyroscope precession, etc., to update the knowledge of the relevant concepts and laws of mechanics; 2) to intensify the educational and cognitive activity of students, to increase their motivation for learning and its effectiveness; 3) to master the method of computer modeling, get acquainted with the solution of various professional tasks, explain the algorithms used and the simulation results at the school or pedagogical university level; 4) to develop the physical and algorithmic thinking, the ability to use the coordinate method and to perform information processing; 5) to organize an interactive dialogue between the student and the computer program; 6) to visualize information about the state of the object, build the trajectory of its points, the dependency graphs of coordinates and speeds on time; 7) to create various animations of body movement, to form a visual image of the corresponding phenomena; 8) to create problematic situations that require editing a computer program, changing system parameters, initial conditions and external influences; 9) to deepen the inter-subject connections between mathematics, physics and computer science.

Healthcare systems are a very important part of the economy of any country and for the public health. The IoT-based monitoring system for patients with paralysis, which helps to promote the health condition of a patient with paralysis, in addition to the day-to-day life. India has suffered a stroke, the incidence is much higher than that of the more developed countries, it is home to around 2.1 million Indians suffered from the boom of the (lame) per year. If a patient is suffering from a paralysis attack in all or any part of the body can be turned off in order to move in, which means that their movement is restricted and they can barely communicate with anyone at all, because they can't talk like a normal person. Raccoons will find it difficult to understand what they are saying, and help them deal with their day-to-day needs, such as food, water, etc.). At present, work is in progress on the review of the motion parameters on the legs, arms, and head of the paralytics. This paper investigates the development of an integrated and portable prototype is a model of a system for the monitoring of the various movements of the body, spinal cord injuries, with the help of sensors. The tests were carried out by placing the sensors on the head, arm, and leg of the paralyzed patient the data received from these sensors are sent to the raspberry pi 3 model. In the Android app, you'll receive a verbal warning, and if the patient is in need of help via Bluetooth, which, in turn, is connected to the raspberry pi.

Gesture is recognized as part of and integral to cognition. The value of gesture for learning is contingent on how it gathers meaning against the ground of other relevant resources in the setting—i...

In this study, we investigate developmental patterns of body movements that may play roles in normal embryonic growth using direct recordings of chick embryos during the early stages of development (72–125 h of incubation). In three individuals, embryonic body movements occurred intermittently and irregularly after 72-h incubation, and frequencies and sizes of movements increased with embryonic growth. Additionally, patterns of the body movements became periodic at 85 h in all individuals, and cyclic periods of body movements were then shortened and periodicities of movements gradually disappeared with embryonic development. If cyclic body movements are a common requirement during normal chick embryo growth, continuous monitoring of body movements could be used to predict abnormal embryonic growth. Future studies are required to investigate distinctive body movements and developmental patterns during embryonic growth in disease models and under various environmental conditions, such as under hypoxia.

The purpose of this research was to investigate the use of body movements as a teaching tool in the Malaysian primary school's children group singing lessons. This study used a qualitative methodology. Semi-structured interviews were conducted with six music teachers. Data was analysed using a theme-based analysis of the narratives. The findings demonstrated that body movements helped the children to improve their vocal techniques and achieve a more successful sound in group singing. Music teachers also stated that emphasis on body movement activities in the class helped children to be more involved in singing. Such movements require to be encouraged to improve the quality of music learning. Training programmes for music teachers also require to incorporate body movements as a teaching tool for effective group singing.

The standardized sizes used in the garment industry do not cover the range of individual differences in body shape for most people, leading to ill-fitting clothes, high return rates and overproduction. Recent research efforts in both industry and academia, therefore, focus on virtual try-on and on-demand fabrication of individually fitting garments. We propose an interactive design tool for creating custom-fit garments based on 3D body scans of the intended wearer. Our method explicitly incorporates transitions between various body poses to ensure a better fit and freedom of movement. The core of our method focuses on tools to create a 3D garment shape directly on an avatar without an underlying sewing pattern, and on the adjustment of that garment's rest shape while interpolating and moving through the different input poses. We alternate between cloth simulation and rest shape adjustment based on stretch to achieve the final shape of the garment. At any step in the real-time process, we allow for interactive changes to the garment. Once the garment shape is finalized for production, established techniques can be used to parameterize it into a 2D sewing pattern or transform it into a knitting pattern., Computer Graphics Forum, 42 (1), ISSN:1467-8659, ISSN:0167-7055

At present, the body recognition detection of athletes is mostly technical recognition, and the detection of exercise state is less, and the related research is basically blank. Based on this, based on BP neural network algorithm, this study develops athletes’ motion capture based on wearable inertial sensors, and builds a wireless signal transmission scheme based on sensor system. At the same time, this paper constructs the coordinate system to complete the attitude angle settlement and motion recognition and combines the athlete’s actual situation to establish the athlete’s limb trajectory calculation model and analyzes the athletes’ movement patterns. In addition, this paper combines neural network algorithm to analyze, and builds a neural network based athlete body motion recognition model, and analyzes the model effectiveness through simulation system. Studies have shown that when using time domain features+trajectory features as neural network inputs, the hand recognition rate is somewhat improved compared to the use of only time domain features as neural network inputs. It can be seen that the algorithm model of this study has certain validity and can be used as a reference for subsequent related research gradient theory.

The aim of this paper is to show how compact mmWave radar systems, attached to a person, can take advantage of their movements, in order to significantly improve the image resolution by means of synthetic aperture radar (SAR) techniques. Thus, high resolution imaging becomes possible even with tiny on-chip systems, opening up a horizon of possibilities to assist visually impaired people. The approach is illustrated for movements in terms of arm swings. For this purpose, it is firstly shown that the information from an inertial measurement unit (IMU) can be used to provide a trajectory estimation, which is enough to apply synthetic aperture radar processing. As a consequence, the resolution of the radar is greatly improved when compared with that achieved considering a single position. In order to avoid the continuously growing position error, commonly observed in dead-reckoning systems relying just on IMU, zero velocity updates (ZUPT) will be systematically applied. For doing so, two different zero velocity detectors (ZVD) are considered, one based on acceleration data and other on velocity estimation. Although the positioning information is extracted from the IMU, demonstrating the enhanced obstacle detection capabilities obtained with this approach, other sensors that can be carried by a person and that are capable of providing trajectory estimations to combine measurements acquired with compact mmWave radars could have been used. The system is tested using different targets and a good performance comparable to the one achieved by using a high accuracy reference positioning system is attained. The compactness of the system enables the possibility of using it as a wearable device, e.g., attached to the wrist. This shows that an IMU is enough for tracking relatively simple movements, though more complex positioning systems can be used for more general actions.

We present a passive ultra-high frequency (UHF) radiofrequency identification (RFID)-based strain sensor, which is designed for simple and efficient body movement monitoring. This RFID platform is fabricated from electro-textile materials and can thus be seamlessly integrated into our everyday clothing. The sensor platform has an integrated reference tag, in order to avoid the effects of reflections or external disturbances on the sensor tag performance. This sensor platform prototype has an on-body read range of 1 meter in a normal office environment with an off-the-shelf RFID reader. The wireless performance of the sensor tag has a significant change caused by arm elongation. Thus, the sensor functionality can be based on variation of the sensor tag’s backscattered power percentage ( $\Delta {P}{\%}$ ). Based on the preliminary results achieved in this study, this passive and cost-effective sensor platform could be an efficient future way to turn human gestures into inputs for digital devices.

Vection is defined as an illusory self-motion sensation induced in stationary observers that can be experienced in a real/virtual world. Vection, as a result of immersion in virtual reality (VR) environments, can subsequently lead to a sense of inability to maintain postural control and cause cybersickness symptoms. The multisensory integration of visual and vestibular (balance) information plays a vital role in vection. The etiology of vection perception, as well as, the vestibular response change while experiencing vection is poorly understood. This study explores vestibular response change following vection in 20 individuals (10 females, 26.45 ± 4.40 (SD) years). Vection was induced in participants using an immersive VR roller-coaster. The vestibular response was measured simultaneously using a noninvasive method called Electrovestibulography (EVestG). The detected field potentials and the time intervals between the field potentials were extracted from the recorded EVestG signals corresponding to four segments of the VR roller-coaster trajectory namely Stationary, Up movement, Down movement, and slopes and turns (Mix). The results show that the Stationary segment is significantly different (P

Virtual vehicles (e.g., driving video games) can give rise to visually induced motion sickness. Typically, people drive virtual vehicles. In the present study, we investigated motion sickness among participants who were exposed to virtual vehicles as passengers; that is, they observed vehicle motion, but did not control it. We also asked how motion sickness and the postural precursors of motion sickness might be influenced by participants' previous experience of driving physical vehicles. Participants viewed a recording of a virtual automobile in a driving video game. Drivers were young adults with several years of experience driving physical automobiles, while non-drivers were individuals in the same age group who did not have a driver's license and had never driven an automobile. During exposure to the virtual vehicle, we monitored movement of the head and torso. The independent measures included the incidence and severity of motion sickness. After exposure to the virtual vehicle, the incidence and severity of motion sickness did not differ between Drivers and Non-Drivers. By contrast, postural movement differed between participants who later became motion sick and those who did not. In addition, during exposure to the virtual vehicle, physical driving experience was related to patterns of postural activity that preceded motion sickness. The results are consistent with the postural instability theory of motion sickness, and illuminate relations between the control of physical and virtual vehicles.

The COVID-19 pandemic swept the world at the beginning of 2020. Countries launched into a state of emergency, involving lockdown and quarantine. When social distancing became the “new normal,” people were forced to stay in, working and communicating remotely at home. For many, an oppressive feeling of “being trapped” creates inertia, a strange lack of adequate energy to even move. Bodies no longer function in a social context. Participating in the Massive and Microscopic sense-making project (Markham & Harris) prompted me to engage in autoethnographic self-exploration of my own embodiment in relation to the pandemic, asking questions such as: How do we go about becoming reconnected again? How can we use movement and the body as a curriculum to learn and unlearn ourselves?

Unlike general ventilation, personalized ventilation can improve thermal comfort and conserve energy based on individual differences. It can also provide every individual the ability to control fresh air exposure and ensure good indoor air quality. However, determining how to improve air supply efficiency while avoiding a draft sensation is still a difficult question. This paper introduces a body movement-based personalized targeted air supply device. Two indices, size target value T s and velocity target value T r , are introduced to evaluate the degree to which the created flow field reaches the desired level. Additionally, the air supply effect of the device is compared with that of other devices. This research shows that the personalized targeted air supply device can successfully deliver air to the target area and improve air supply accessibility in the target area. The multinozzle coupling air supply mode produces a flow field air velocity of approximately 0.3 m/s, thus effectively avoiding a draft sensation. Compared with that of other personalized nozzles, the energy consumption is reduced significantly, by 88.2%, while the air supply accessibility can be increased by 48% with equivalent energy consumption.

This paper seeks a theoretical approach to the body best suited to the anthropological study of body movement in the martial arts. It follows the development of the anthropological attitude to the body from its formative period up until the present day, and this is done from the position of the “embodiment” paradigm as a theoretical orientation which enables a deeper understanding of the connection between specific cultural environments in which martial arts evolve and the meaning of the movements themselves. The introductory section provides a brief summary of “unembodied” theoretical schools of thought, which laid the foundations for the “somatic revolution”. The paper then considers the authors who have made the most significant contribution to the anthropological study of embodiment, and looks at four theoretical perspectives on the body, namely, the physical, socio-cultural, embodied, and dynamic embodiment perspectives. The paper also considers the question of overcoming the ontological body-mind dichotomy, which is the legacy of Cartesian dualism. Particular attention has been given to the embodiment and dynamic embodiment perspectives, and to their potential for application in anthropological studies of martial physical practices, illustrated with concrete examples.

Stable trajectory tracking is one of the most important challenges in wearable rehabilitation robots design. Feedback controllers play a critical role to achieve this goal. There are different options to implement such a controller, e.g. PID, Fuzzy and hybrid systems. Traditionally, the performance of controllers is examined by try and error on hardware setups. It can be easily translated to more wasted time and cost. In this paper, we tried to make the design process more dynamic and efficient in. first, we proposed a simulation tool (based on NEXUSES® and MATLAB®) for a wearable rehabilitation robot with two links and 3 degrees-of-freedom, which duplicates elbow, wrist, and shoulder movement in 2-D space. Then, by the help of the tool we studied the benefits and functionality of one type of controller: classical PID. Our simulation result is verified that the Classical PID is improved the stability of trajectory tracking. QC 20200930

The aim of this study is to evaluate the effectiveness of an intervention program, based on music, movement, and dance, aimed at improving motor skills in children at risk of developmental coordination disorder (DCD). Participants comprised 70 primary school children (47 boys/23 girls) aged between 6 and 8 years. Participants were given the Spanish adaptation of the Movement Assessment Battery for Children–Second Edition (MABC-2) to identify difficulties and initially classify into three groups: a group of 17 at risk of DCD who participated in the intervention program (Experimental Group), a group of 18 at risk of DCD who did not participate (Control Group with Risk) and a group of 35 children, with scores higher than the cutoff point (Control Group without Risk). The results show a significant improvement in the motor skills of children who participated in the intervention program, while those in the Control Group showed no significant changes in the second evaluation compared with the first. Likewise, it could be affirmed that this intervention based on group activities involving music, movement, and dance is a very successful blend for motor improvement in children with coordination problems, due to the combination of cognitive stimulation areas and techniques.

The recent advancement of motion tracking technology offers better treatment tools for conditions, such as movement disorders, as the outcome of the rehabilitation could be quantitatively defined. The accurate and fast angular information output of the inertial measurement unit tracking systems enables the collection of accurate kinematic data for clinical assessment. This article presents a study of a low-cost microelectromechanical system inertial measurement unit-based tracking system in comparison with the conventional optical tracking system. The system consists of seven microelectromechanical system inertial measurement units, which could be mounted on the lower limbs of the subjects. For the feasibility test, 10 human participants were instructed to perform three different motions: walking, running, and fencing lunges when wearing specially designed sleeves. The subjects’ lower body movements were tracked using our inertial measurement unit-based system and compared with the gold standard—the NDI Polaris Vega optical tracking system. The results of the angular comparison between the inertial measurement unit and the NDI Polaris Vega optical tracking system were as follows: the average cross-correlation value was 0.85, the mean difference of joint angles was 2.00°, and the standard deviation of joint angles was ± 2.65°. The developed microelectromechanical system–based tracking system provides an alternative low-cost solution to track joint movement. Moreover, it is able to operate on an Android platform and could potentially be used to assist outdoor or home-based rehabilitation.

Low cardiorespiratory fitness (CRF) increases risk of all-cause mortality and cardiovascular events. Periodic CRF assessment can have an important preventive function. The objective of this study was to develop a protocol-free method to estimate CRF in daily life based on heart rate (HR) and body acceleration measurements. Acceleration and HR data were collected from 37 subjects (men = 49%) while they performed a standardized laboratory activity protocol (sitting, walking, running, cycling) and during a 5-day free-living monitoring period. CRF was determined by oxygen uptake (V̇o2max) during maximal exercise testing. A doubly labeled water-validated equation was used to predict total energy expenditure (TEE) from acceleration data. A fitness index was defined as the ratio between TEE and HR (TEE-pulse). Activity recognition techniques were used to process acceleration features and classify sedentary, ambulatory, and other activity types. Regression equations based on TEE-pulse data from each activity type were developed to predict V̇o2max. TEE-pulse measured within each activity type of the laboratory protocol was highly correlated with V̇o2max ( r from 0.74–0.91). Averaging the outcome of each activity-type specific equation based on TEE-pulse from the laboratory data led to accurate estimates of V̇o2max [root mean square error (RMSE): 300 mL O2/min, or 10%]. The difference between laboratory and free-living determined TEE-pulse was 3.7 ± 11% ( r = 0.85). The prediction method preserved the prediction accuracy when applied to free-living data (RMSE: 367 mL O2/min, or 12%). Measurements of body acceleration and HR can be used to predict V̇o2max in daily life. Activity-specific prediction equations are needed to achieve highly accurate estimates of CRF. NEW & NOTEWORTHY This is among the very few studies validating, in free-living conditions, a method to estimate cardiorespiratory fitness using heart rate and body acceleration data. A novel parameter called TEE-pulse, which was defined as the ratio between accelerometer-determined energy expenditure and heart rate, was highly correlated with maximal oxygen uptake (V̇o2max). Activity classification and the use of activity-selective prediction equations outperformed previously published methods for estimating V̇o2max from heart rate and acceleration data.

Purpose: This study examined differences in energy expenditure and bodily movement among children of different weight status during exergames that varied in mode and intensity. Methods: Fifty-seven 8- to 12-year-old children including overweight/obesity (n = 28) and normal weight (n = 29) played three 10-minute interval Xbox One exergames (Fruit Ninja, Kung-Fu, and Shape Up) categorized based on predominantly upper-, whole-, or lower-limb movement, respectively. The authors measured bodily movement through accelerometry and obtained energy expenditure and metabolic equivalent (MET) via indirect calorimetry. Results: Energy expended during gameplay was the highest in Shape Up (P P P P > .05). Based on the MET cut-points, overweight/obese children spent more time at light intensity (P P P P P = .03). Conclusion: Although children in both groups expended similar energy relative to their body mass during gameplay, overweight/obese children spent more time at light intensity but less time at vigorous intensity with fewer movements especially while playing a lower limb–controlled exergame.

Coma is the state of anesthetized or unconscious state where the patient doesn’t respond to the actions or any activities. Since the patients are in unconscious state the patients at tender or the doctors don’t have time to observe the patient activities continuously. In order to overcome this problem an IoT based system is designed to monitor the body movements and the actions of the coma patient using various sensors. The equipment has been planned that utilizes Arduino controller board. In addition with the Arduino board various sensors have been associated through the controller board interface. The sensors values will be transferred to the cloud server. The movement of eye ball retina and motion of the body detected by the wearable motion sensors state are monitored. By using the sensors it is helpful for the doctor and the specialist about the well-being condition of the patients and continuously monitoring their physical condition. The challenging task is focused on the limitation of the existing techniques are discussed.

In the context of the Internet of Things (IoT) healthcare, biophysical features collected during sleep needs robust analysis methods to be efficiently used to detect sleep disorders. In this paper, analysis methods using a limited number of input variables (cardiac, respiratory, and body movement) have been used to perform the classification of sleep stages. The efficiency of each classification method has been compared to a reference method that combines a large number of biophysical features referred to as PolySomnoGraphy (PSG). Five classical machine learning methods were evaluated by testing their accuracy on the same collected data. Finally, using a neural network with a short memory method, the classification task fitted 91.34% of the PSG classification.

Interaction between children’s developmental degree of music and their musical expression continued to intrigue researchers. Currently, one noteworthy element will be to analyze such interaction from quantitative approach and to detect some predictive methodology to replicate such interaction statistically. In this study, the author extracted developmental characteristics of musical expressions in early childhood from viewpoints of elements of body movement, and applied a classification of machine learning based method on those feature quantities acquired from the participant children. Classification models were applied to the feature quantity for 3-year-old, 4-year-old, and 5-year-old in 2 nursery schools in 2016, 2 kindergartens in 2017 and a certified facility in 2018 utilizing 3D motion capture. In order to highlight developmental degree and to extract feature quantity, a three-way non-repeated ANOVA was applied and a statistically significant difference was observed in the movement data analyzed of the moving average of distance such as pelvis and right hand, the moving average of acceleration such as right hand, and the movement smoothness of right foot. The author classified the developmental degree of children’s musical expression by machine learning classifiers using the feature quantities of motion capture data after let classifiers train with categorical variables of developmental degree evaluated by the author with simultaneously recorded video. The author report here that the best classifier is Multilayer Perceptron Neural Network and the second best is Boosted Trees. The sensitivity result showed that the movement of the pelvis was strongly related to the musical development degree. In order to improve classification accuracy, classifiers such as deep learning etc. can be considered in addition to the further review of kinetic feature quantities, or increase of training sample data etc.

The ability to correctly perceive time and extract accurate timing information is crucial during social interaction. In fact, several activities during social interaction, such as appropriate feedback, turn-taking, coordination with peers, and even empathy and engagement exhibition directly depend on it. One of the aspects of cognitive malfunctioning in children with Autistic Spectrum Disorders is time perception deficit. Learning to pay attention to and correctly assess timing is thus a critical first step to improve social skills for children with Autism. In this paper, we present a novel sensing system and algorithm for estimating a subject's rhythmic motion timing from visual information using Recurrent Neural Network (RNN) coupled with FFT. This system will enable a robot saxophonist to estimate the rhythmic period from a child's motion during a robot-based music therapy session. Fast-Fourier- Transform (FFT) is an algorithm widely applied in rhythmic body movement detection, due to advantages such low computation and easy integration. However, long transient time delay is a critical limitation, reducing the correct motion timing estimation during period transitions. The novel system presented in this article is shown to significantly reduce transient time delay. The results of both a simulation and an evaluation experiment show that, compared with FFT processing alone, this algorithm gives a better performance due to its smaller average offset error and shorter transient time delay, allowing a more precise assessment of the child's synchronization response.

Review of: Site, Dance and Body: Movement Materials and Corporeal Engagement, Victoria Hunter (2021)London: Palgrave Macmillan, XII, 279 pp.,ISBN 978-3-03064-800-8, h/bk, GBP 89.99, e-book, GBP 71.50

Nonverbal behaviour plays an important role in journalism because of its heavy reliance on visual forms of communication. In the first section of this chapter, academic research on nonverbal behaviour is discussed in terms of the following topics: the communication of emotion and interpersonal relationships, the synchronization of nonverbal behaviour and speech, deception detection, and communications skills training. The second section focusses on the use of visuals and nonverbal behaviour in two specific journalistic contexts: print journalism and the television news. Illustrative examples are discussed, based on the impact of photographs of the Vietnam War and the dead Syrian boy Aylan Kurdi; celebrity photographs are also considered. There follows an analysis of the television news, focussed primarily on recent changes in audiovisual editing techniques, and this is illustrated by a case study—an analysis of audiovisual news coverage of the British parliamentary expenses scandal of 2009.

Background Growing evidence suggests that movement abnormalities occur prior to the onset of psychosis. Innovations in technology and software provide the opportunity for a fine-tuned and sensitive measurement of observable behavior that may be particularly useful to detecting the subtle movement aberrations present during the prodromal period. Methods In the present study, 54 youth at ultrahigh risk (UHR) for psychosis and 62 healthy controls participated in structured clinical interviews to assess for an UHR syndrome. The initial 15 min of the baseline clinical interview was assessed using Motion Energy Analysis (MEA) providing frame-by-frame measures of total movement, amplitude, speed, and variability of both head and body movement separately. Results Result showed region-specific group differences such that there were no differences in head movement but significant differences in body movement. Specifically, the UHR group showed greater total body movement and speed of body movements, and lower variation in body movement compared to healthy controls. However, there were no significant associations with positive, negative or disorganized symptom domains. Conclusion This study represents an innovative perspective on gross motor function in the UHR group. Importantly, the automated approach used in this study provides a sensitive and objective measure of body movement abnormalities, potentially guiding novel assessment and prevention of symptom development in those at risk for psychosis.