RFID Backscatter Based Sport Motion Sensing Using ECOC-Based SVM

With the advent of the 5G era, radio frequency identification (RFID) has been widely applied in various fields as one of the key technologies for the Internet of Things (IoT) to realize the Internet of Everything (IoE). In recent years, RFID-based motion sensing has emerged as an important research area with great potential for development. In this paper, an RFID backscatter sport motion sensing scheme is proposed, which effectively solves the multi-classification problem by using the received signal strength (RSS) of the backscattered RFID and the error correcting output coding (ECOC)-based support vector machine (SVM). We conduct extensive experiments to validate the effectiveness of the proposed scheme, in which the signal intensities of different types of action poses are collected and the SVM is used as the classification algorithm to achieve high classification accuracies.

Social modulation of oogenesis and egg laying in Drosophila melanogaster

Being part of a group facilitates cooperation between group members but also creates competition for resources. This is a conundrum for gravid females, whose future offspring benefit from being in a group only if there are enough resources relative to group size. Females may therefore be expected to modulate reproductive output depending on social context. In the fruit fly Drosophila melanogaster, females actively attract conspecifics to lay eggs on the same resources, generating groups in which individuals may cooperate or compete. The genetic tractability of this species allows dissecting the mechanisms underlying physiological adaptation to social context. Here, we show that females produce eggs increasingly faster as group size increases. By laying eggs faster when grouped than when isolated, females reduce competition between offspring and increase offspring survival. In addition, grouped females lay eggs during the day, while isolated females lay them at night. We show that responses to the presence of others requires visual input and that flies from any sex, mating status, or species can trigger these responses. The mechanisms of this modulation of egg laying by group is connected to a lifting of the inhibition of light on oogenesis and egg laying, possibly mediated in part by an increase in juvenile hormone activity. Because modulation of reproduction by social context is a hallmark of animals with higher levels of sociality, our findings in a species considered solitary question the validity of this nomenclature and suggest a widespread and profound influence of social context on reproduction.

Construct validation of a general movement competence assessment utilising active video gaming technology

Introduction: The assessment of children's motor competence is an important concern as physical inactivity has been linked with poor movement quality and aspects of well-being such as low self-esteem. The General Movement Competence Assessment (GMCA) is a new instrument that was developed using active video gaming technology. Methods: Confirmatory factor analysis was conducted to examine the internal validity of the GMCA in a sample of 253 typically developing children (135 boys and 118 girls), aged 7-12 years old (9.9 ± 1.6 years). Further, a second-order confirmatory factor analysis examined how the four constructs fit onto the higher-order variable of movement competence. Results: Results revealed that the first-order four-construct model of the GMCA was a good fit (CFI 0.98; TLI 0.98; RMSEA 0.05). The second-order confirmatory factor analysis revealed that the four constructs loaded directly onto movement competence. It accounted for 95.44% of the variance which is approximately 20% more than the first-order model. The internal structure of the GMCA identified four constructs of movement competence (i.e., stability, object-control, locomotion and dexterity) based on the study sample. Discussion: Performance trends in the general movement competence assessment support empirical evidence that movement competence improves as children age. Results suggest that active video games have considerable potential to help assess general motor competency in the wider population. Future work may consider the sensitivity of motion-sensing technologies in detecting developmental changes over time.

A New Hybrid Lead-Free Metal Halide Piezoelectric for Energy Harvesting and Human Motion Sensing

Hybrid organic-inorganic piezoelectrics have attracted attention due to their simple synthesis, mechanical flexibility, and designability, which have promising application potential in flexible sensing and self-powered energy harvesting devices. Although some hybrid piezoelectrics are discovered, most of their structures are limited by the perovskite-type and often contain lead. Herein, the synthesis, structure, and piezoelectric properties of a new hybrid lead-free metal halide, (BTMA)₂ CoBr₄ (BTMA = benzyltrimethylammonium) are reported. The experimental and theoretical results demonstrate that this material simply composed of [CoBr₄ ]^2- tetrahedra and BTMA⁺ cations exhibits significant piezoelectricity (d₂₂ = 5.14, d₂₅ = 12.40 pC N^-1 ), low Young's and shear moduli (4.11-17.56 GPa; 1.86-7.91 GPa). Moreover, the (BTMA)₂ CoBr₄ /PDMS (PDMS = polydimethylsiloxane) composite thin films are fabricated and optimized. The 10% (BTMA)₂ CoBr₄ /PDMS-based flexible devices show attractive performance in energy harvesting with an open-circuit voltage of 19.70 V, short-circuit current of 4.24 µA, and powder density of 11.72 µW cm^-2 , catching up with those of piezoelectric ceramic composites. Meanwhile, these film devices show excellent capability in accurately sensing human body motions, such as finger bending and tapping. This work demonstrates that (BTMA)₂ CoBr₄ and related piezoelectric lead-free halides can be promising molecular materials in modern energy and sensing applications.

Statistical Extraction Method for Revealing Key Factors from Posture before Initiating Successful Throwing Technique in Judo

Many methods such as biomechanics and coaching have been proposed to help people learn a certain movement. There have been proposals for methods to discover characteristics of movement based on information obtained from videos and sensors. Especially in sports, it is expected that these methods can provide hints to improve movement skills. However, conventional methods focus on individual movements, and do not consider cases where external factors influence the movement, such as combat sports. In this paper, we propose a novel method called the Extraction for Successful Movement method (XSM method). Applying the method, this paper focuses on throwing techniques in judo to discover key factors that induce successful throwing from the postures right before initiating the throwing techniques. We define candidate factors by observing the video scenes where the throwing techniques are successfully performed. The method demonstrates the significance of the key factors according to the predominance of factors by χ2 test and residual analysis. Applying the XSM method to the dataset obtained from the videos of the Judo World Championships, we demonstrate the validity of the method with discussing the key factors related to the successful throwing techniques.

Stimuli-Responsive Electrospun Piezoelectric Mats of Ethylene-co-vinyl Acetate-Millable Polyurethane-Nanohydroxyapatite Composites

Piezoelectric materials have gained interest among materials scientists as body motion sensors and energy harvesters on account of their fast responsiveness and substantial output signals. In this work, piezoelectric polymer mats have been fabricated from ethylene-co-vinyl acetate-millable polyurethane/nanohydroxyapatite (EVA-MPU/nHA) composite systems by employing the electrospinning technique. The ferro-piezoelectric features of the samples were confirmed from the butterfly loops of electrostatic force microscopy (EFM) amplitude signals as well as through the hysteresis curves of the EFM phase recorded with the assistance of dynamic-contact EFM. Piezoelectric responses of the samples to random finger tapping were evaluated after fabricating a simple device prototype connected to an oscilloscope. The efficacy of the mats to generate a voltage in response to activities such as mechanical bending, movement of throat muscles while drinking, movement of elbow joints, air blowing, and so forth has also been investigated. The results suggest the promising possibility of fabricating user-friendly piezoelectric mats out of the EVA-MPU/nHA system for physiological motion-sensing applications.

Low-Complexity Design and Validation of Wireless Motion Sensor Node to Support Physiotherapy

We present a motion sensor node to support physiotherapy, based on an Inertial Measurement Unit (IMU). The node has wireless interfaces for both data exchange and charging, and is built based on commodity components. It hence provides an affordable solution with a low threshold to technology adoption. We share the hardware design and explain the calibration and validation procedures. The sensor node has an autonomy of 28 h in operation and a standby time of 8 months. On-device sensor fusion yields static results of on average 3.28° with a drift of 2° per half hour. The final prototype weighs 38 g and measures ø6 cm × 1.5 cm. The resulting motion sensor node presents an easy to use device for both live monitoring of movements as well as interpreting the data afterward. It opens opportunities to support and follow up treatment in medical cabinets as well as remotely.

Rehabilitation Exergames: Use of Motion Sensing and Machine Learning to Quantify Exercise Performance in Healthy Volunteers

Background

Performing physiotherapy exercises in front of a physiotherapist yields qualitative assessment notes and immediate feedback. However, practicing the exercises at home lacks feedback on how well patients are performing the prescribed tasks. The absence of proper feedback might result in patients performing the exercises incorrectly, which could worsen their condition. We present an approach to generate performance scores to enable tracking the progress by both the patient at home and the physiotherapist in the clinic.

Objective

This study aims to propose the use of 2 machine learning algorithms, dynamic time warping (DTW) and hidden Markov model (HMM), to quantitatively assess the patient’s performance with respect to a reference.

Methods

Movement data were recorded using a motion sensor (Kinect V2), capable of detecting 25 joints in the human skeleton model, and were compared with those of a reference. A total of 16 participants were recruited to perform 4 different exercises: shoulder abduction, hip abduction, lunge, and sit-to-stand exercises. Their performance was compared with that of a physiotherapist as a reference.

Results

Both algorithms showed a similar trend in assessing participant performance. However, their sensitivity levels were different. Although DTW was more sensitive to small changes, HMM captured a general view of the performance, being less sensitive to the details.

Conclusions

The chosen algorithms demonstrated their capacity to objectively assess the performance of physical therapy. HMM may be more suitable in the early stages of a physiotherapy program to capture and report general performance, whereas DTW could be used later to focus on the details. The scores enable the patient to monitor their daily performance. They can also be reported back to the physiotherapist to track and assess patient progress, provide feedback, and adjust the exercise program if needed.

Hierarchically Rough Structured and Self-Powered Pressure Sensor Textile for Motion Sensing and Pulse Monitoring

Nowadays, real-time human motion sensing and pulse monitoring can provide significant basis for health assessment and medical diagnosis. Nevertheless, it is still a big challenge to design a lightweight, flexible, and energy-sustainable pressure sensor with high sensitivity and breathability. Here, we fabricated a triboelectric all-fiber structured pressure sensor via a facile electrospinning technique. The constructed sensor textile holds a composite structure made up of a polyvinylidene fluoride/Ag nanowire nanofibrous membrane (NFM), an ethyl cellulose NFM, and two layers of conductive fabrics. This wearable device with high shape adaptability exhibited excellent sensing capability because of the introduced hierarchically rough structure on the nanofibers. The sensitivity can reach up to 1.67 and 0.20 V·kPa^-1 in the pressure range of 0-3 and 3-32 kPa, respectively. The fabricated sensor textile also showed a superior mechanical stability even after continuous operation of 7200 working cycles. This sensor textile was easily conformable on different desired body parts for dynamic motion sensing and real-time pulse monitoring. It can work in a self-powered manner to detect and quantify various human motions associated with joints, such as elbows, knees, and ankles. Additionally, it can be placed on the carotid artery to capture the pulse signals, serving as a reliable way to reflect the state of health. This work has great possibilities to promote the rapid advancement and broad applications of multifunctional pressure sensors and next-generation wearable electronics.

Dual-Core Capacitive Microfiber Sensor for Smart Textile Applications

Wearable sensors for smart textile applications have garnered tremendous interest in recent years and can have enormous potential for human machine interfaces and digital health monitoring. Here, we report a soft capacitive microfiber sensor that can be woven seamlessly into textiles for strain measurement. Comprising a dual-lumen elastomeric microtube and liquid metallic alloy, the microfiber sensor enables continual strain perception even after being completely severed. In addition, our microfiber sensor is highly stretchable and flexible and exhibits tunable sensitivity, excellent linearity, a fast response, and negligible hysteresis. More importantly, the microfiber sensor is minimally affected by train rate and compression during strain sensing. Even under drastic environmental changes, the microfiber sensor presents good electrical stability. By integrating the microfiber sensor imperceptibly with textiles, we devise smart textile wearables to interpret hand gestures, detect limb motion, and monitor respiration rate. We believe that this sensor presents enormous potential in unobtrusive continuous health monitoring.

Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system

Work metabolism (WM) can be accurately estimated by oxygen consumption (VO₂), which is commonly assessed by heart rate (HR) in field studies. However, the VO₂-HR relationship is influenced by individual capacity and activity characteristics. The purpose of this study was to evaluate three models for estimating WM compared with indirect calorimetry, during simulated work activities. The techniques were: the HR-Flex model; HR branched model, combining HR with hip-worn accelerometers (ACC); and HR + arm-leg ACC model, combining HR with wrist- and thigh-worn ACC. Twelve participants performed five simulated work activities and three submaximal tests. The HR + arm-leg ACC model had the overall best performance with limits of agreement (LoA) of -3.94 and 2.00 mL/min/kg, while the HR-Flex model had -5.01 and 5.36 mL/min/kg and the branched model, -6.71 and 1.52 mL/min/kg. In conclusion, the HR + arm-leg ACC model should, when feasible, be preferred in wearable systems for WM estimation. Practitioner Summary: Work with high energy demand can impair employees' health and life quality. Three models were evaluated for estimating work metabolism during simulated tasks. The model combining heart rate, wrist- and thigh-worn accelerometers showed the best accuracy. This is, when feasible, suggested for wearable systems to assess work metabolism.

Visual Calibration for Multiview Laser Doppler Speed Sensing

We present a novel calibration method for a multi-view laser Doppler speed sensing (MLDSS) system. In contrast with the traditional method where only the laser geometry is independently calibrated, the proposed method simultaneously optimizes all the laser parameters and directly associates the parameters with a motion sensing model. By jointly considering the consistency among laser Doppler velocimetry, the laser geometry and a visual marker tracking system, the proposed calibration method further boosts the accuracy of MLDSS. We analyzed the factors influencing the precision, and quantitatively evaluated the efficiency of the proposed method on several data sets.

Mitigation of CSI Temporal Phase Rotation with B2B Calibration Method for Fine-Grained Motion Detection Analysis on Commodity Wi-Fi Devices

Limitations of optical devices for motion sensing such as small coverage, sensitivity to obstacles, and privacy exposure result in the need for improvement. As motion sensing based on radio frequency signals is not constrained by the limitation above, channel state information (CSI) from Wi-Fi devices could be used to improve sensing performance under the above circumstances. Unfortunately, CSI phase cannot be practically obtained due to the temporal phase rotation generated from Wi-Fi chips. Therefore, it would be rather complicated to realize motion analysis, especially the direction of motion. To mitigate the issue, this paper proposes a CSI calibration method that employs a back-to-back channel between Wi-Fi transceivers for phase rotation removal while preserving the original CSI phase. Through experiment, calibrated CSI showed a high similarity to the channel without phase rotation measured using a Vector Network Analyzer (VNA). Another experiment was conducted to observe Doppler frequency due to simple hand gestures using the Wavelet transform. A visual analysis revealed that the Doppler frequency of calibrated CSI could correctly capture the motion pattern. To the best of the authors' knowledge, this is the first calibration method that maintains the original CSI and is applicable for in-depth motion analysis.

Motion-Based Immunological Detection of Zika Virus Using Pt-Nanomotors and a Cellphone

Zika virus (ZIKV) infection is an emerging pandemic threat to humans that can be fatal in newborns. Advances in digital health systems and nanoparticles can facilitate the development of sensitive and portable detection technologies for timely management of emerging viral infections. Here we report a nanomotor-based bead-motion cellphone (NBC) system for the immunological detection of ZIKV. The presence of virus in a testing sample results in the accumulation of platinum (Pt)-nanomotors on the surface of beads, causing their motion in H₂O₂ solution. Then the virus concentration is detected in correlation with the change in beads motion. The developed NBC system was capable of detecting ZIKV in samples with virus concentrations as low as 1 particle/μL. The NBC system allowed a highly specific detection of ZIKV in the presence of the closely related dengue virus and other neurotropic viruses, such as herpes simplex virus type 1 and human cytomegalovirus. The NBC platform technology has the potential to be used in the development of point-of-care diagnostics for pathogen detection and disease management in developed and developing countries.

Use of GPS tracking collars and accelerometers for rangeland livestock production research

Over the last 20 yr, global positioning system (GPS) collars have greatly enhanced livestock grazing behavior research. Practices designed to improve livestock grazing distribution can now be accurately and cost effectively monitored with GPS tracking. For example, cattle use of feed supplement placed in areas far from water and on steep slopes can be measured with GPS tracking and corresponding impacts on distribution patterns estimated. Ongoing research has identified genetic markers that are associated with cattle spatial movement patterns. If the results can be validated, genetic selection for grazing distribution may become feasible. Tracking collars have become easier to develop and construct, making them significantly less expensive, which will likely increase their use in livestock grazing management research. Some research questions can be designed so that dependent variables are measured by spatial movements of livestock, and in such cases, GPS tracking is a practical tool for conducting studies on extensive and rugged rangeland pastures. Similarly, accelerometers are changing our ability to monitor livestock behavior. Today, accelerometers are sensitive and can record movements at fine temporal scales for periods of weeks to months. The combination of GPS tracking and accelerometers appears to be useful tools for identifying changes in livestock behavior that are associated with livestock diseases and other welfare concerns. Recent technological advancements may make real-time or near real-time tracking on rangelands feasible and cost-effective. This would allow development of applications that could remotely monitor livestock well-being on extensive rangeland and notify ranchers when animals require treatment or other management.

Development of a Wearable Instrumented Vest for Posture Monitoring and System Usability Verification Based on the Technology Acceptance Model

Body posture and activity are important indices for assessing health and quality of life, especially for elderly people. Therefore, an easily wearable device or instrumented garment would be valuable for monitoring elderly people's postures and activities to facilitate healthy aging. In particular, such devices should be accepted by elderly people so that they are willing to wear it all the time. This paper presents the design and development of a novel, textile-based, intelligent wearable vest for real-time posture monitoring and emergency warnings. The vest provides a highly portable and low-cost solution that can be used both indoors and outdoors in order to provide long-term care at home, including health promotion, healthy aging assessments, and health abnormality alerts. The usability of the system was verified using a technology acceptance model-based study of 50 elderly people. The results indicated that although elderly people are anxious about some newly developed wearable technologies, they look forward to wearing this instrumented posture-monitoring vest in the future.

Computer-Guided Deep Brain Stimulation Programming for Parkinson's Disease

OBJECTIVE

Pilot study to evaluate computer-guided deep brain stimulation (DBS) programming designed to optimize stimulation settings using objective motion sensor-based motor assessments.

MATERIALS AND METHODS

Seven subjects (five males; 54-71 years) with Parkinson's disease (PD) and recently implanted DBS systems participated in this pilot study. Within two months of lead implantation, the subject returned to the clinic to undergo computer-guided programming and parameter selection. A motion sensor was placed on the index finger of the more affected hand. Software guided a monopolar survey during which monopolar stimulation on each contact was iteratively increased followed by an automated assessment of tremor and bradykinesia. After completing assessments at each setting, a software algorithm determined stimulation settings designed to minimize symptom severities, side effects, and battery usage.

RESULTS

Optimal DBS settings were chosen based on average severity of motor symptoms measured by the motion sensor. Settings chosen by the software algorithm identified a therapeutic window and improved tremor and bradykinesia by an average of 35.7% compared with baseline in the "off" state (p < 0.01).

CONCLUSIONS

Motion sensor-based computer-guided DBS programming identified stimulation parameters that significantly improved tremor and bradykinesia with minimal clinician involvement. Automated motion sensor-based mapping is worthy of further investigation and may one day serve to extend programming to populations without access to specialized DBS centers.

Visual motion-sensitive neurons in the bumblebee brain convey information about landmarks during a navigational task

Bees use visual memories to find the spatial location of previously learnt food sites. Characteristic learning flights help acquiring these memories at newly discovered foraging locations where landmarks—salient objects in the vicinity of the goal location—can play an important role in guiding the animal's homing behavior. Although behavioral experiments have shown that bees can use a variety of visual cues to distinguish objects as landmarks, the question of how landmark features are encoded by the visual system is still open. Recently, it could be shown that motion cues are sufficient to allow bees localizing their goal using landmarks that can hardly be discriminated from the background texture. Here, we tested the hypothesis that motion sensitive neurons in the bee's visual pathway provide information about such landmarks during a learning flight and might, thus, play a role for goal localization. We tracked learning flights of free-flying bumblebees (Bombus terrestris) in an arena with distinct visual landmarks, reconstructed the visual input during these flights, and replayed ego-perspective movies to tethered bumblebees while recording the activity of direction-selective wide-field neurons in their optic lobe. By comparing neuronal responses during a typical learning flight and targeted modifications of landmark properties in this movie we demonstrate that these objects are indeed represented in the bee's visual motion pathway. We find that object-induced responses vary little with object texture, which is in agreement with behavioral evidence. These neurons thus convey information about landmark properties that are useful for view-based homing.

Spectral analyses of wrist motion in individuals poststroke: the development of a performance measure with promise for unsupervised settings

BACKGROUND

Upper extremity use in daily life is a critical ingredient of continued functional recovery after cerebral stroke. However, time-evolutions of use-dependent motion quality are poorly understood due to limitations of existing measurement tools.

OBJECTIVE

Proof-of-concept study to determine if spectral analyses explain the variability of known temporal kinematic movement quality (ie, movement duration, number of peaks, jerk) for uncontrolled reach-to-grasp tasks.

METHODS

Ten individuals with chronic stroke performed unimanual goal-directed movements using both hands, with and without task object present, wearing accelerometers on each wrist. Temporal and spectral measures were extracted for each gesture. The effects of performance condition on outcome measures were determined using 2-way, within subject, hand (nonparetic vs paretic) × object (present vs absent) analysis of variance. Regression analyses determined if spectral measures explained the variability of the temporal measures.

RESULTS

There were main effects of hand on all 3 temporal measures and main effects of object on movement duration and peaks. For the paretic limb, spectral measures explain 41.2% and 51.1% of the variability in movement duration and peaks, respectively. For the nonparetic limb, spectral measures explain 40.1%, 42.5%, and 27.8% of the variability of movement duration, peaks, and jerk, respectively.

CONCLUSIONS

Spectral measures explain the variability of motion efficiency and control in individuals with stroke. Signal power from 1.0 to 2.0 Hz is sensitive to changes in hand and object. Analyzing the evolution of this measure in ambient environments may provide as yet uncharted information useful for evaluating long-term recovery.