Secondary Chemical Cross-Linking to Improve Mechanical Properties in a Multifaceted Biocompatible Strain Sensor

A new conductive and transparent organohydrogel is developed with high stretchability, excellent mechanical, self-healing, antifreezing, and adhesive properties. A simple one-pot polymerization method is used to create polyacrylamide cross-linked through N,N'-methylenebis(acrylamide) (MBAA) and divinylbenzene (DVB). The dual chemical cross-linked gel network is complemented by several physical cross-links via hydrogen bonding and π-π interaction. Multiple chemical and physical cross-links are used to construct the gel network that allows toughness (171 kPa), low modulus (≈45 kPa), excellent stretchability (>1100%), and self-healing ability. The use of appropriate proportions of the water/glycerol binary solvent system ensures efficient environment tolerance (-20 to 40 °C). Phytic acid is used as a conductive filler that provides excellent conductivity and contributes to the physical cross-linking. Dopamine is incorporated in the gel matrix, which endows excellent adhesive property of the gel. The organohydrogel-based strain sensors are developed with state-independent properties, highly linear dependence, and excellent antifatigue performance (>100 cycles). Moreover, during the practical wearable sensing tests, human motions can be detected, including speaking, smiling, and joint movement. Additionally, the sensor is biocompatible, indicating the potential applications for the next generation of epidermal sensors.

Computational Study of a Motion Sensor to Simultaneously Measure Two Physical Quantities in All Three Directions for a UAV

Cross-axis sensitivity is generally undesirable, and lower values are required for the accurate performance of a thermal accelerometer. In this study, errors in devices are utilized to simultaneously measure two physical quantities of an unmanned aerial vehicle (UAV) in the X-, Y-, and Z-directions, i.e., where three accelerations and three rotations can also be simultaneously measured using a single motion sensor. The 3D structures of thermal accelerometers were designed and simulated in a FEM simulator using commercially available FLUENT 18.2 software Obtained temperature responses were correlated with input physical quantities, and a graphical relationship was created between peak temperature values and input accelerations and rotations. Using this graphical representation, any values of acceleration from 1g to 4g and rotational speed from 200 to 1000°/s can be simultaneously measured in all three directions.

Graphene and Liquid Metal Integrated Multifunctional Wearable Platform for Monitoring Motion and Human-Machine Interfacing

Motion sensors are an essential component of many electronic systems. However, the development of inertial motion sensors based on fatigue-free soft proof mass has not been explored extensively in the field of soft electronics. Nontoxic gallium-based liquid metals are an emerging class of material that exhibit attractive electromechanical properties, making them excellent proof mass materials for inertial sensors. Here, we propose and demonstrate a fully soft laser-induced graphene (LIG) and liquid metal-based inertial sensor integrated with temperature, humidity, and breathing sensors. The inertial sensor design confines a graphene-coated liquid metal droplet inside a fluidic channel, rolling over LIG resistive electrode. The proposed sensor architecture and material realize a highly mobile proof mass and a vibrational space for its oscillation. The inertial sensor exhibits a high sensitivity of 6.52% m^-1 s² and excellent repeatability (over 12 500 cycles). The platform is fabricated using a scalable, rapid laser writing technique and integrated with a programmable system on a chip (PSoC) to function as a stand-alone system for real-time wireless monitoring of movement patterns and the control of a robotic arm. The developed printed inertial platform is an excellent candidate for the next-generation of wearables motion tracking platforms and soft human-machine interfaces.

Development of a Moving Baseline RTK/Motion Sensor-Integrated Positioning-Based Autonomous Driving Algorithm for a Speed Sprayer

To address problems such as pesticide poisoning and accidents during pest control work and to enable efficient work in this area, the development of a competitively prices speed sprayer with autonomous driving is required. Accordingly, in order to contribute to developing the commercialization of a low-cost autonomous driving speed sprayer, we developed a positioning algorithm and an autonomous driving-based spraying algorithm by using two low-cost global navigation satellite system (GNSS) modules and a low-cost motion sensor. In order to provide stable navigation solutions from the autonomous driving hardware despite disturbances from the electromagnetic field generated by the spraying device, the proposed positioning algorithm, a moving baseline (MB) real-time kinematic (RTK)/motion sensor-integrated positioning algorithm, was developed using a loosely coupled extended Kalman filter. To compare the yaw estimation performance provided by the MB RTK positioning technique, yaw was calculated by post-processing with two types of positioning algorithms: the MB RTK/motion sensor-integrated positioning algorithm and the GNSS RTK/motion sensor-integrated positioning algorithm. In the static test, the precision of the yaw provided by the MB RTK/motion sensor-integrated positioning algorithm was 0.14°, but with the GNSS RTK/motion sensor-integrated positioning algorithm, the precision of the yaw was 4.53°. The static test results confirmed that the proposed positioning algorithm using the yaw provided by the MB RTK positioning technique based on two GNSS modules for measurement, precisely estimated the yaw even when the spray engine was operating. To perform autonomous driving and spraying, an autonomous driving-based spraying algorithm was developed using the MB RTK/motion sensor-integrated positioning algorithm. As a result of two performance tests based on the proposed algorithm in an orchard, autonomous driving and spraying were stably performed according to the set autonomous driving route and spraying method, and the root mean square (RMS) of the path-following error was 0.06 m.

Detecting and Visualizing Stops in Dance Training by Neural Network Based on Velocity and Acceleration

Various genres of dance, such as Yosakoi Soran, have contributed to the health of many people and contributed to their sense of belonging to a community. However, due to the effects of COVID-19, various face-to-face activities have been restricted and group dance practice has become difficult. Hence, there is a need to facilitate remote dance practice. In this paper, we propose a system for detecting and visualizing the very important dance motions known as stops. We measure dance movements by motion capture and calculate the features of each movement based on velocity and acceleration. Using a neural network to learn motion features, the system detects stops and visualizes them using a human-like 3D model. In an experiment using dance data, the proposed method obtained highly accurate stop detection results and demonstrated its effectiveness as an information and communication technology support for remote group dance practice.

Human Lower Limb Motion Capture and Recognition Based on Smartphones

Human motion recognition based on wearable devices plays a vital role in pervasive computing. Smartphones have built-in motion sensors that measure the motion of the device with high precision. In this paper, we propose a human lower limb motion capture and recognition approach based on a Smartphone. We design a motion logger to record five categories of limb activities (standing up, sitting down, walking, going upstairs, and going downstairs) using two motion sensors (tri-axial accelerometer, tri-axial gyroscope). We extract the motion features and select a subset of features as a feature vector from the frequency domain of the sensing data using Fast Fourier Transform (FFT). We classify and predict human lower limb motion using three supervised learning algorithms: Naïve Bayes (NB), K-Nearest Neighbor (KNN), and Artificial Neural Networks (ANNs). We use 670 lower limb motion samples to train and verify these classifiers using the 10-folder cross-validation technique. Finally, we design and implement a live detection system to validate our motion detection approach. The experimental results show that our low-cost approach can recognize human lower limb activities with acceptable accuracy. On average, the recognition rate of NB, KNN, and ANNs are 97.01%, 96.12%, and 98.21%, respectively.

Self-Standing, Photothermal-Actuating, and Motion-Monitoring Janus Films One-Pot Synthesized by Green Carboxymethyl Glucomannan/Liquid Metal Nanoinks

Downsizing bulk liquid metals (LM) at the nanometer scale with biocompatibility and multifunction is a key process for electronic or medical applications. Here, we report a stable and green LM aqueous colloidal ink by wrapping eutectic gallium-indium alloys (EGaIn) with carboxymethyl glucomannan (CGM) derived from radiata pine chip, which is capable of being prepared into a free-standing, photothermal-actuating, and motion-monitoring Janus film. With the assistance of CGM, the bulk EGaIn was ultrasonicated into stable nanodroplets (∼500 nm) with a typical "core-shell" structure, in which the colloidal inks can be stored for more than 1 week under room temperature. The stable CGM/EGaIn inks can be patterned on different substrates to form coating layers or self-assembled into free-standing Janus films with high mechanical strength and modulus (∼94 MPa and ∼3.8 GPa) by density deposition. Such a Janus film with anisotropic thermal conductivity made it a potential photothermal actuator. In addition, the biocompatible film demonstrated both high conductivity and large resistance variation in response to strain change (gauge factor >500), allowing for human motion monitoring. This work provides a new prospect for the development of biocompatible and high-performance nano-LM materials.

Changes in systemic inflammation after pulmonary rehabilitation in patients with COPD and severe physical inactivity - an exploratory study

Background: Severe physical inactivity (SPI) in patients with COPD is associated with a poor prognosis. It is unknown whether there is a link between SPI and systemic inflammation, and if systemic inflammation in SPI changes following pulmonary rehabilitation (PR).

Methods: A prospective, observational study of patients referred for at least 7 weeks of PR comprising 2 h of exercise therapy and education twice weekly. At baseline and after PR, daily physical activity level (PAL) was measured with a validated activity monitor, SenseWear^® as well as systemic inflammation: b-eosinophils, p-fibrinogen, p-CRP, s-IL-6 and s-CD 163. SPI was defined as PAL <1.4.

Results: At baseline, SPI was present in 31 of the 57 patients included, and 23% (7/31) improved to non-SPI after PR. We observed no differences between patients with SPI and non-SPI, except baseline plasma fibrinogen level was slightly yet significantly higher in patients with SPI (median 13.3 [6.2–23.6] vs 11.2 [6.5–16.7] µmol/l) but change in fibrinogen levels differed insignificantly between patients who improved to non-SPI at follow-up compared to patients with persistent SPI (−0.6 [−16.9–9.9] vs −0.4 [−11.2–1.2] µmol/l).

Conclusion: SPI in COPD appears not to be associated with a distinct inflammatory profile compared to less sedentary COPD patients attending pulmonary rehabilitation. Currently biomarkers have no role in the detection of SPI in COPD.

Performance Index for Extrinsic Calibration of LiDAR and Motion Sensor for Mapping and Localization

Light Detection and Ranging (LiDAR) is a sensor that uses a laser to represent the surrounding environment in three-dimensional information. Thanks to the development of LiDAR, LiDAR-based applications are being actively used in autonomous vehicles. In order to effectively use the information coming from LiDAR, extrinsic calibration which finds the translation and the rotation relationship between LiDAR coordinate and vehicle coordinate is essential. Therefore, many studies on LiDAR extrinsic calibration are steadily in progress. The performance index (PI) of the calibration parameter is a value that quantitatively indicates whether the obtained calibration parameter is similar to the true value or not. In order to effectively use the obtained calibration parameter, it is important to validate the parameter through PI. Therefore, in this paper, we propose an algorithm to obtain the performance index for the calibration parameter between LiDAR and the motion sensor. This performance index is experimentally verified in various environments by Monte Carlo simulation and validated using CarMaker simulation data and real data. As a result of verification, the PI of the calibration parameter obtained through the proposed algorithm has the smallest value when the calibration parameter has a true value, and increases as an error is added to the true value. In other words, it has been proven that PI is convex to the calibration parameter. In addition, it is able to confirm that the PI obtained using the proposed algorithm provides information on the effect of the calibration parameters on mapping and localization.

Performance Evaluation of an Autonomously Driven Agricultural Vehicle in an Orchard Environment

To address the problems of inefficient agricultural production and labor shortages, there has been active research to develop autonomously driven agricultural machines, using advanced sensors and ICT technology. Autonomously driven speed sprayers can also reduce accidents such as the pesticide poisoning of farmers, and vehicle overturn that frequently occur during spraying work in orchards. To develop a commercial, autonomously driven speed sprayer, we developed a prototype of an autonomously driven agricultural vehicle, and conducted performance evaluations in an orchard environment. A prototype of the agricultural vehicle was created using a rubber-tracked vehicle equipped with two AC motors. A prototype of the autonomous driving hardware consisted of a GNSS module, a motion sensor, an embedded board, and an LTE module, and it was made for less than $1000. Additional software, including a sensor fusion algorithm for positioning and a path-tracking algorithm for autonomous driving, were implemented. Then, the performance of the autonomous driving agricultural vehicle was evaluated based on two trajectories in an apple farm. The results of the field test determined the RMS, and the maximums of the path-following errors were 0.10 m, 0.34 m, respectively.

Synthetic Generation of Passive Infrared Motion Sensor Data Using a Game Engine

Quantifying the number of occupants in an indoor space is useful for a wide variety of applications. Attempts have been made at solving the task using passive infrared (PIR) motion sensor data together with supervised learning methods. Collecting a large labeled dataset containing both PIR motion sensor data and ground truth people count is however time-consuming, often requiring one hour of observation for each hour of data gathered. In this paper, a method is proposed for generating such data synthetically. A simulator is developed in the Unity game engine capable of producing synthetic PIR motion sensor data by detecting simulated occupants. The accuracy of the simulator is tested by replicating a real-world meeting room inside the simulator and conducting an experiment where a set of choreographed movements are performed in the simulated environment as well as the real room. In 34 out of 50 tested situations, the output from the simulated PIR sensors is comparable to the output from the real-world PIR sensors. The developed simulator is also used to study how a PIR sensor’s output changes depending on where in a room a motion is carried out. Through this, the relationship between sensor output and spatial position of a motion is discovered to be highly non-linear, which highlights some of the difficulties associated with mapping PIR data to occupancy count.

Evaluating the benefit of hearing aids with motion-based beamformer adaptation in a real-world setup

OBJECTIVE

Conventional directional hearing aid microphone technology may obstruct listening intentions when the talker and listener walk side by side. The purpose of the current study was to evaluate hearing aids that use a motion sensor to address listening needs during walking.

DESIGN

Each participant completed two walks in randomised order, one walk with each of two hearing aid programs: (1) conventional beamformer adaptation that activated an adaptive, multiband beamformer in loud environments and (2) motion-based beamformer adaptation that activated a pinna-mimicking microphone setting when walking was detected. Participants walked along a pre-defined track and completed tasks assessing speech understanding and environmental awareness.

STUDY SAMPLE

Participants were 22 older adults with moderate-to-severe hearing loss and experience using hearing aids.

RESULTS

More participants preferred the motion-based than conventional beamformer adaptation for speech understanding, environmental awareness, overall listening, and sound quality (p < 0.05). Measures of speech understanding (p < 0.01) and localisation of sound stimuli (p < 0.05) were significantly better with motion-based than conventional beamformer adaptation.

CONCLUSIONS

The results suggest that hearing aid users can benefit from beamforming that uses motion sensor input to adapt the signal processing according to the user's activity. The real-world setup of this study had limitations.

Measurement of Human Walking Movements by Using a Mobile Health App: Motion Sensor Data Analysis

Background

This study presents a new approach to measure and analyze the walking balance of humans by collecting motion sensor data in a smartphone.

Objective

We aimed to develop a mobile health (mHealth) app that can measure the walking movements of human individuals and analyze the differences in the walking movements of different individuals based on their health conditions. A smartphone’s motion sensors were used to measure the walking movements and analyze the rotation matrix data by calculating the variation of each xyz rotation, which shows the variables in walking-related movement data over time.

Methods

Data were collected from 3 participants, that is, 2 healthy individuals (1 female and 1 male) and 1 male with back pain. The participant with back pain injured his back during strenuous exercise but he did not have any issues in walking. The participants wore the smartphone in the middle of their waistline (as the center of gravity) while walking. They were instructed to walk straight at their own pace in an indoor hallway of a building. The walked a distance of approximately 400 feet. They walked for 2-3 minutes in a straight line and then returned to the starting location. A rotation vector in the smartphone, calculated by the rotation matrix, was used to measure the pitch, roll, and yaw angles of the human body while walking. Each xyz-rotation vector datum was recalculated to find the variation in each participant’s walking movement.

Results

The male participant with back pain showed a diminished level of walking balance with a wider range of xyz-axis variations in the rotations compared to those of the healthy participants. The standard deviation in the xyz-axis of the male participant with back pain was larger than that of the healthy male participant. Moreover, the participant with back pain had the widest combined range of right-to-left and forward-to-backward motions. The healthy male participant showed smaller standard deviation while walking than the male participant with back pain and the female healthy participant, indicating that the healthy male participant had a well-balanced walking movement. The walking movement of the female healthy participant showed symmetry in the left-to-right (x-axis) and up-to-down (y-axis) motions in the x-y variations of rotation vectors, indicating that she had lesser bias in gait than the others.

Conclusions

This study shows that our mHealth app based on smartphone sensors and rotation vectors can measure the variations in the walking movements of different individuals. Further studies are needed to measure and compare walking movements by age, gender, as well as types of health problems or disease. This app can help in finding differences in gait in people with diseases that affect gait.

Accelerometer Calibration: The Importance of Considering Functionality

Purpose

To compare the accuracy and precision of a hip-worn accelerometer to predict energy cost during structured activities across motor performance and disease conditions.

Methods

118 adults self-identifying as healthy (n = 44) and those with arthritis (n = 23), multiple sclerosis (n = 18), Parkinson's disease (n = 17), and stroke (n =18) underwent measures of motor performance and were categorized into groups: Group 1, usual; Group 2, moderate impairment; and Group 3, severe impairment. The participants completed structured activities while wearing an accelerometer and a portable metabolic measurement system. Accelerometer-predicted energy cost (metabolic equivalent of tasks [METs]) were compared with measured METs and evaluated across functional impairment and disease conditions. Statistical significance was assessed using linear mixed effect models and Bayesian information criteria to assess model fit.

Results

All activities' accelerometer counts per minute (CPM) were 29.5-72.6% less for those with disease compared with those who were healthy. The predicted MET bias was similar across disease, -0.49 (-0.71, -0.27) for arthritis, -0.38 (-0.53, -0.22) for healthy, -0.44 (-0.68, -0.20) for MS, -0.34 (-0.58, -0.09) for Parkinson's, and -0.30 (-0.54, -0.06) for stroke. For functional impairment, there was a graded reduction in CPM for all activities: Group 1, 1,215 CPM (1,129, 1,301); Group 2, 789 CPM (695, 884); and Group 3, 343 CPM (220, 466). The predicted MET bias revealed similar results across the Group 1, -0.37 METs (-0.52, -0.23); Group 2, -0.44 METs (-0.60, -0.28); and Group 3, -0.33 METs (-0.55, -0.13). The Bayesian information criteria showed a better model fit for functional impairment compared with disease condition.

Conclusion

Using functionality to improve accelerometer calibration could decrease variability and warrants further exploration to improve accelerometer prediction of physical activity.

Sensor Fusion of Motion-Based Sign Language Interpretation with Deep Learning

Sign language was designed to allow hearing-impaired people to interact with others. Nonetheless, knowledge of sign language is uncommon in society, which leads to a communication barrier with the hearing-impaired community. Many studies of sign language recognition utilizing computer vision (CV) have been conducted worldwide to reduce such barriers. However, this approach is restricted by the visual angle and highly affected by environmental factors. In addition, CV usually involves the use of machine learning, which requires collaboration of a team of experts and utilization of high-cost hardware utilities; this increases the application cost in real-world situations. Thus, this study aims to design and implement a smart wearable American Sign Language (ASL) interpretation system using deep learning, which applies sensor fusion that “fuses” six inertial measurement units (IMUs). The IMUs are attached to all fingertips and the back of the hand to recognize sign language gestures; thus, the proposed method is not restricted by the field of view. The study reveals that this model achieves an average recognition rate of 99.81% for dynamic ASL gestures. Moreover, the proposed ASL recognition system can be further integrated with ICT and IoT technology to provide a feasible solution to assist hearing-impaired people in communicating with others and improve their quality of life.

Modeling of the MET Sensitive Element Conversion Factor on the Intercathode Distance

MET sensors for measuring motion parameters are used in many scientific and technical fields. Meanwhile, the geometries of the transforming cell applied practically are far from optimal, and the influence of many geometric parameters on the sensitivity has not been studied. These parameters include the intercathode distance in a four-electrode conversion cell. In this paper, a mathematical model that allows calculating the behavior of the conversion coefficient depending on the frequency for a cell with flat electrodes at different intercathode distances is constructed. The stationary current is shown to decrease monotonically with the decreasing intercathode distance at the constancy of other system parameters. At the same time, the signal current decreases in the low-frequency region and increases in the high-frequency range. Taking into account the results obtained, practically speaking, it is advisable to reduce the intercathode distance to the technologically possible minimum, which makes the frequency response more uniform and reduces the current consumed by the sensitive element.

Sensing a problem: Proof of concept for characterizing and predicting agitation

INTRODUCTION

Agitation, experienced by patients with dementia, is difficult to manage and stressful for caregivers. Currently, agitation is primarily assessed by caregivers or clinicians based on self-report or very brief periods of observation. This limits availability of comprehensive or sensitive enough reporting to detect early signs of agitation or identify its precipitants. The purpose of this article is to provide proof of concept for characterizing and predicting agitation using a system that continuously monitors patients' activities and living environment within memory care facilities.

METHODS

Continuous and unobtrusive monitoring of a participant is achieved using behavioral sensors, which include passive infrared motion sensors, door contact sensors, a wearable actigraphy device, and a bed pressure mat sensor installed in the living quarters of the participant. Environmental sensors are also used to continuously assess temperature, light, sound, and humidity. Episodes of agitation are reported by nursing staff. Data collected for 138 days were divided by 8-hour nursing shifts. Features from agitated shifts were compared to those from non-agitated shifts using t-tests.

RESULTS

A total of 37 episodes of agitation were reported for a male participant, aged 64 with Alzheimer's disease, living in a memory care unit. Participant activity metrics (eg, transitions within the living room, sleep scores from the bedmat, and total activity counts from the actigraph) significantly correlated with occurrences of agitation at night (P < 0.05). Environmental variables (eg, humidity) also correlated with the occurrences of agitation at night (P < 0.05). Higher activity levels were also observed in the evenings before agitated nights.

DISCUSSION

A platform of sensors used for unobtrusive and continuous monitoring of participants with dementia and their living space seems feasible and shows promise for characterization of episodes of agitation and identification of behavioral and environmental precipitants of agitation.

Wearable Motion Sensor Device to Facilitate Rehabilitation in Patients With Shoulder Adhesive Capsulitis: Pilot Study to Assess Feasibility

BACKGROUND

Adhesive capsulitis (AC) of the shoulder is a common disorder that painfully reduces the shoulder range of motion (ROM) among middle-aged individuals. Although physical therapy with home-based exercises is widely advised to restore ROM in the treatment of AC, clinical results vary owing to inconsistent patient compliance.

OBJECTIVE

In this study, we aimed to verify the feasibility of a treatment model that involves applying a wearable motion sensor device to assist patients conduct home-based exercises to improve training compliance and the accuracy of exercises, with the ultimate goal of improving the functional recovery of patients with AC.

METHODS

The motion sensor device was comprised of inertial measurement unit-based sensors and mobile apps for patients and physicians, offering shoulder mobility tracing, home-based exercise support, and progress monitoring. The interrater reliability of shoulder mobility measurement using the motion sensor device on 10 healthy participants and 15 patients with AC was obtained using an intraclass correlation coefficient analysis and compared with the assessments performed by two highly experienced physicians. A pilot prospective control trial was then carried out to allocate the 15 patients with AC to two groups: home-based exercise group and motion sensor-assisted rehabilitation group. Changes in active and passive shoulder ROM, pain and functional scores, and exercise completion rates were compared between the groups during a treatment period of 3 months.

RESULTS

Shoulder ROM, as measured using the motion sensor device, exhibited good to excellent reliability based on the comparison with the measurements of two physicians (intraclass correlation coefficient range, 0.771 to 0.979). Compared with patients with AC in the home-based exercise group, those in the motion sensor-assisted rehabilitation group exhibited better shoulder mobility and functional recovery and a higher exercise completion rate during and after 3 months of rehabilitation.

CONCLUSIONS

Motion sensor device-assisted home-based rehabilitation for the treatment of AC is a useful treatment model for telerehabilitation that enhances the compliance of patients through training, thus improving functional recovery. This helps overcome important obstacles in physiotherapy at home by providing comprehensible and easily accessible exercise instructions, enhancing compliance, ensuring the correctness of exercise, and monitoring the progress of patients.

Step-Based Metrics and Overall Physical Activity in Children With Overweight or Obesity: Cross-Sectional Study

Background

Best-practice early interventions to increase physical activity (PA) in children with overweight and obesity should be both feasible and evidence based. Walking is a basic human movement pattern that is practical, cost-effective, and does not require complex movement skills. However, there is still a need to investigate how much walking—as a proportion of total PA level—is performed by children who are overweight and obese in order to determine its utility as a public health strategy.

Objective

This study aimed to (1) investigate the proportion of overall PA indicators that are explained by step-based metrics and (2) study step accumulation patterns relative to achievement of public health recommendations in children who are overweight and obese.

Methods

A total of 105 overweight and obese children (mean 10.1 years of age [SD 1.1]; 43 girls) wore hip-worn accelerometers for 7 days. PA volumes were derived using the daily average of counts per 15 seconds, categorized using standard cut points for light-moderate-vigorous PA (LMVPA) and moderate-to-vigorous PA (MVPA). Derived step-based metrics included volume (steps/day), time in cadence bands, and peak 1-minute, 30-minute, and 60-minute cadences.

Results

Steps per day explained 66%, 40%, and 74% of variance for counts per 15 seconds, LMVPA, and MVPA, respectively. The variance explained was increased up to 80%, 92%, and 77% by including specific cadence bands and peak cadences. Children meeting the World Health Organization recommendation of 60 minutes per day of MVPA spent less time at zero cadence and more time in cadence bands representing sporadic movement to brisk walking (ie, 20-119 steps/min) than their less-active peers.

Conclusions

Step-based metrics, including steps per day and various cadence-based metrics, seem to capture a large proportion of PA for children who are overweight and obese. Given the availability of pedometers, step-based metrics could be useful in discriminating between those children who do or do not achieve MVPA recommendations.

Trial Registration

ClinicalTrials.gov NCT02295072; https://clinicaltrials.gov/ct2/show/NCT02295072

Assessment of the Status of Patients with Parkinson's Disease Using Neural Networks and Mobile Phone Sensors

Parkinson's disease (PD) is one of the most common chronic neurological diseases and one of the significant causes of disability for middle-aged and elderly people. Monitoring the patient's condition and its compliance is the key to the success of the correction of the main clinical manifestations of PD, including the almost inevitable modification of the clinical picture of the disease against the background of prolonged dopaminergic therapy. In this article, we proposed an approach to assessing the condition of patients with PD using deep recurrent neural networks, trained on data measured using mobile phones. The data was received in two modes: background (data from the phone's sensors) and interactive (data directly entered by the user). For the classification of the patient's condition, we built various models of the neural network. Testing of these models showed that the most efficient was a recurrent network with two layers. The results of the experiment show that with a sufficient amount of the training sample, it is possible to build a neural network that determines the condition of the patient according to the data from the mobile phone sensors with a high probability.

Piezoelectric sensor based on graphene-doped PVDF nanofibers for sign language translation

The tracking of body motion, such as bending or twisting, plays an important role in modern sign language translation. Here, a subtle flexible self-powered piezoelectric sensor (PES) made of graphene (GR)-doped polyvinylidene fluoride (PVDF) nanofibers is reported. The PES exhibits a high sensitivity to pressing and bending, and there is a stable correlation between bending angle and piezoelectric voltage. The sensitivity can be adjusted by changing the doping concentration of GR. Also, when the PES contacts a source of heat, a pyroelectric signal can be acquired. The positive correlation between temperature and signal can be used to avoid burns. The integrated sensing system based on multiple PESs can accurately recognize the action of each finger in real time, which can be effectively applied in sign language translation. PES-based motion-tracking applications have been effectively used, especially in human-computer interaction, such as gesture control, rehabilitation training, and auxiliary communication.

Measurement of Three-Dimensional Structural Displacement Using a Hybrid Inertial Vision-Based System

Accurate three-dimensional displacement measurements of bridges and other structures have received significant attention in recent years. The main challenges of such measurements include the cost and the need for a scalable array of instrumentation. This paper presents a novel Hybrid Inertial Vision-Based Displacement Measurement (HIVBDM) system that can measure three-dimensional structural displacements by using a monocular charge-coupled device (CCD) camera, a stationary calibration target, and an attached tilt sensor. The HIVBDM system does not require the camera to be stationary during the measurements, while the camera movements, i.e., rotations and translations, during the measurement process are compensated by using a stationary calibration target in the field of view (FOV) of the camera. An attached tilt sensor is further used to refine the camera movement compensation, and better infers the global three-dimensional structural displacements. This HIVBDM system is evaluated on both short-term and long-term synthetic static structural displacements, which are conducted in an indoor simulated experimental environment. In the experiments, at a 9.75 m operating distance between the monitoring camera and the structure that is being monitored, the proposed HIVBDM system achieves an average of 1.440 mm Root Mean Square Error (RMSE) on the in-plane structural translations and an average of 2.904 mm RMSE on the out-of-plane structural translations.

Stretchable Micromotion Sensor with Enhanced Sensitivity Using Serpentine Layout

The application of the serpentine mesh layout in stretchable electronics provides a feasible method to achieve the desired stretchability by structural design instead of modifying the intrinsic mechanical properties of the applied materials. However, previous works using the serpentine layout mainly focused on the optimization of structural stretchability. In this paper, the serpentine mesh design concept is used to transform the high-performance but hard-to-stretch piezoelectric film into a stretchable form. The serpentine layout design strategies for the piezoelectric film, which aim at not only desired stretchability but also high utilization of the strain in the piezoelectric film during deformation, are discussed with experimental and computational results. A stretchable micromotion sensor with high sensitivity is realized using the piezoelectric film with a serpentine layout. Human voice recognition applications of the sensor, including speech pattern recognition with machine learning, are demonstrated with the sensor integrated with a wireless module. The stretchable micromotion sensor with a serpentine layout illustrates the broader application of serpentine layout design in the functional materials of stretchable electronics, which can further extend the range of available functional materials for novel stretchable electronic devices.

Developing a telerehabilitation programme for postoperative recovery from knee surgery: specifications and requirements

INTRODUCTION

Telerehabilitation programmes have been attracting increasing attention as a potential alternative to conventional rehabilitation. Video conferencing can facilitate communication between healthcare professionals and patients. However, in certain cases, video conferencing may face practical limitations. As an alternative to real-time conferencing, sensor-based technologies can transmit the acquired data to healthcare providers. This study aimed to design and develop a sensor-based telerehabilitation programme and to outline the corresponding requirements for such a system.

DEVELOPMENT

The development of the sensor-based telerehabilitation programme was carried out based on user needs. The programme includes a portable platform for the patient as well as a web-based platform for the healthcare professional, thus allowing for an individualised rehabilitation programme. Communication, training, reporting, and information services were provided for the patients. Moreover, the portability and usability of the programme were enhanced by utilising the system in offline mode as well.

APPLICATION

The programme is currently being tested in the North Denmark Region to assess the feasibility and acceptance of a telerehabilitation programme as an alternative solution to the self-training programme for patients who have been discharged from knee surgery. The preliminary results of our assessment showed a high level of acceptance among the users.

DISCUSSION

In this study, a semi-online sensor-based telerehabilitation programme was tested. It is argued that a similar sensor-based telerehabilitation programme can be utilised as an alternative solution for self-training rehabilitation in the future; however; further studies and development are required to ensure the quality and reliability of sensor-based services.

Touchpoint-Tailored Ultrasensitive Piezoresistive Pressure Sensors with a Broad Dynamic Response Range and Low Detection Limit

Wearable pressure sensors with high sensitivity, broad dynamic response range, and low detection limit are highly desirable to enable the applications in electronic skins and soft robotics. In this work, we report a high-performance wearable pressure sensor based on microstructured polydimethylsiloxane (PDMS)/Ag and rough polyimide/Au interdigital electrodes. By tailoring the touchpoints, the resulting pressure sensors show ultrahigh sensitivity (259.32 kPa^-1 in the range of 0-2.5 kPa), broad dynamic response range (0-54 kPa), fast response (∼200 μs), and low detection limit (0.36 Pa). Furthermore, the effect of different sensor structural configurations, PDMS geometrical feature, and Ag thickness on the performance of the pressure sensors are systematically investigated. Thanks to these merits, the fabricated pressure sensor is capable of real-time monitoring pulse wave and can act as artificial skin for robot hand to detect weak pressure changes, leading to the great application promise in the fields of biomedical, real-time health monitoring, and intelligent robot.

Yield Visualization Based on Farm Work Information Measured by Smart Devices

This paper proposes a new approach to visualizing spatial variation of plant status in a tomato greenhouse based on farm work information operated by laborers. Farm work information consists of a farm laborer’s position and action. A farm laborer’s position is estimated based on radio wave strength measured by using a smartphone carried by the farm laborer and Bluetooth beacons placed in the greenhouse. A farm laborer’s action is recognized based on motion data measured by using smartwatches worn on both wrists of the farm laborer. As experiment, harvesting information operated by one farm laborer in a part of a tomato greenhouse is obtained, and the spatial distribution of yields in the experimental field, called a harvesting map, is visualized. The mean absolute error of the number of harvested tomatoes in each small section of the experimental field is 0.35. An interview with the farm manager shows that the harvesting map is useful for intuitively grasping the states of the greenhouse.

A Hybrid Textile Electrode for Electrocardiogram (ECG) Measurement and Motion Tracking

Wearable sensors have great potential uses in personal health monitoring systems, in which textile-based electrodes are particularly useful because they are comfortable to wear and are skin and environmentally friendly. In this paper, a hybrid textile electrode for electrocardiogram (ECG) measurement and motion tracking was introduced. The hybrid textile electrode consists of two parts: A textile electrode for ECG monitoring, and a motion sensor for patient activity tracking. In designing the textile electrodes, their performance in ECG measurement was investigated. Two main influencing factors on the skin-electrode impedance of the electrodes were found: Textile material properties, and electrode sizes. The optimum textile electrode was silver plated, made of a high stitch density weft knitted conductive fabric and its size was 20 mm × 40 mm. A flexible motion sensor circuit was designed and integrated within the textile electrode. Systematic measurements were performed, and results have shown that the hybrid textile electrode is capable of recording ECG and motion signals synchronously, and is suitable for ambulatory ECG measurement and motion tracking applications.

Iono-Elastomer-Based Wearable Strain Sensor with Real-Time Thermomechanical Dual Response

An ultrastretchable iono-elastomer with resistance sensitive to both elongation strain and temperature has been developed by hierarchical self-assembly of an end functionalized triblock copolymer in a protic ionic liquid (ethylammonium nitrate) followed by cross-linking. Small-angle X-ray scattering experiments in situ with uniaxial elongation reveal a nanoscale microstructural transition of the hierarchically self-assembled cross-linked micelles that is responsible for the material's remarkable mechanical and ionic conductivity responses. The results show that the intermicelle distance extends along the deformation direction while the micelles organize into a long-range ordered face-centered-cubic structure during the uniaxial elongation. Besides good cyclability and resistance to selected physical damage, the iono-elastomer simultaneously achieves an unprecedented combination of high stretchability (340%), highly linear resistance vs elongation strain ( R² = 0.998), and large temperature gauge factor (Δ R/ R = 3.24%/°C@30 °C). Human subject testing demonstrates that the iono-elastomer-based wearable thermomechanical sensor is able to effectively and accurately register both body motion and skin temperature simultaneously.

Cardiac gating using scattering of an 8-channel parallel transmit coil at 7T

PURPOSE

To establish a cardiac signal from scattering matrix or scattering coefficient measurements made on a 7T 8-channel parallel transmit (pTx) system, and to evaluate its use for cardiac gating.

METHODS

Measurements of the scattering matrix and scattering coefficients were acquired using a monitoring pulse sequence and during a standard cine acquisition, respectively. Postprocessing used an independent component analysis and gating feature identification. The effect of the phase of the excitation radiofrequency (RF) field ( B1+ shim) on the cardiac signal was simulated for multiple B1+ shim configurations, and cine images were reconstructed from both the scattering coefficients and electrocardiogram (ECG).

RESULTS

The cardiac motion signal was successfully identified in all subjects with a mean signal-to-noise ratio of 33.1 and 5.7 using the scattering matrix and scattering coefficient measurements, respectively. The dominant gating feature in the cardiac signal was a peak aligned with end-systole that occurred on average at 311 and 391 ms after the ECG trigger, with a mean standard deviation of 13.4 and 18.1 ms relative to ECG when using the scattering matrix and scattering coefficients measurements, respectively. The scattering coefficients showed a dependence on B1+ shim with some shim configurations not showing any cardiac signal. Cine images were successfully reconstructed using the scattering coefficients with minimal differences compared to those using ECG.

CONCLUSION

We have shown that the scattering of a pTx RF coil can be used to estimate a cardiac signal, and that scattering matrix and coefficients can be used to cardiac gate MRI acquisitions with the scattering matrix providing a superior cardiac signal. Magn Reson Med 80:633-640, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Self-Powered Multifunctional Motion Sensor Enabled by Magnetic-Regulated Triboelectric Nanogenerator

With the fast development of the Internet of Things, the requirements of system miniaturization and integration have accelerated research on multifunctional sensors. Based on the triboelectric nanogenerator, a self-powered multifunctional motion sensor (MFMS) is proposed in this work, which is capable of detecting the motion parameters, including direction, speed, and acceleration of linear and rotary motions, simultaneously. The MFMS consists of a triboelectric nanogenerator (TENG) module, a magnetic regulation module, and an acrylic shell. The TENG module is formed by placing a free-standing magnetic disk (MD) on a polytetrafluorethylene (PTFE) plate with six copper electrodes. The movement of the MFMS causes the MD to slide on the PTFE plate and hence excites the electrodes to produce a voltage output. The carefully designed six copper electrodes (an inner circle electrode, an outer circle electrode, and four arc electrodes between them) can distinguish eight directions of movement with the acceleration and determine the rotational speed and direction as well. Besides, the magnetic regulation module is applied here by fixing a magnetic cylinder (MC) in the shell, right under the center of the PTFE plate. Due to the magnetic attraction applied by the MC, the MD will automatically return to the center to prepare for the next round of detection, which makes the proposed sensor much more applicable in practice.

Pitfalls in accelerometer-based measurement of physical activity: The presence of reactivity in an adult population

When a behavior is monitored, it is likely to change, even if no change may be intended. This phenomenon is known as measurement reactivity. We investigated systematic changes in accelerometer-based measures over the days of monitoring as an indicator of measurement reactivity in an adult population. One hundred seventy-one participants from the general population (65% women; mean age = 55 years, range: 42-65 years) wore accelerometers for 7 consecutive days to measure sedentary behavior and physical activity (PA). Latent growth models were used (a) to investigate changes in accelerometer wear time over the measurement days and (b) to identify measurement reactivity indicated by systematic changes in sedentary time (ST), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Over the measurement days, participants reduced accelerometer wear time by trend (rate of change [b] = -4.7 min/d, P = .051, Cohen's d = .38), increased ST (b = 2.4 min/d, P = .018, d = .39), and reduced LPA (b = -2.4 min/d, P = .015, d = .38). Participants did not significantly reduce MVPA (P = .537). Our data indicated that accelerometry might generate reactivity. Small effects on ST and LPA were found. Thus, the validity of accelerometer-based data on ST and LPA may be compromised. Systematic changes observed in accelerometer wear time may further bias accelerometer-based measures. MVPA seems to be less altered due to the presence of an accelerometer.

Simultaneous Detection of Displacement, Rotation Angle, and Contact Pressure Using Sandpaper Molded Elastomer Based Triple Electrode Sensor

In this article, we report on a flexible sensor based on a sandpaper molded elastomer that simultaneously detects planar displacement, rotation angle, and vertical contact pressure. When displacement, rotation, and contact pressure are applied, the contact area between the translating top elastomer electrode and the stationary three bottom electrodes change characteristically depending on the movement, making it possible to distinguish between them. The sandpaper molded undulating surface of the elastomer reduces friction at the contact allowing the sensor not to affect the movement during measurement. The sensor showed a 0.25 mm⁻¹ displacement sensitivity with a ±33 μm accuracy, a 0.027 degree⁻¹ of rotation sensitivity with ~0.95 degree accuracy, and a 4.96 kP⁻¹ of pressure sensitivity. For possible application to joint movement detection, we demonstrated that our sensor effectively detected the up-and-down motion of a human forefinger and the bending and straightening motion of a human arm.

Smart Floor with Integrated Triboelectric Nanogenerator As Energy Harvester and Motion Sensor

A smart floor is demonstrated by integrating a square-frame triboelectric nanogenerator (SF-TENG) into a standard wood floor. The smart floor has two working modes based on two pairs of triboelectric materials: one is purposely chosen polytetrafluoroethylene films and aluminum (Al) balls, and the other is the floor itself and the objects that can be triboelectrically charged, such as basketball, shoe soles, and Scotch tape, etc. Utilizing the Al balls enclosed inside shallow boxes, the smart floor is capable of harvesting vibrational energy and, hence, provides a nonintrusive way to detect sudden falls in elderly people. In addition, when the basketball is bounced repeatedly on the floor, the average output voltage and current are 364 ± 43 V and 9 ± 1 μA, respectively, and 87 serially connected light-emitting diodes can be lit up simultaneously. Furthermore, the friction between the triboelectrically chargeable objects and the floor can also induce an alternating current output in the external circuit without the vibration of the Al balls. Normal human footsteps on the floor produce a voltage of 238 ± 17 V and a current of 2.4 ± 0.3 μA. Therefore, this work presents a smart floor with built-in SF-TENG without compromising the flexibility and stability of the standard wood floor and also demonstrates a way to harvest ambient energy solely by using conventional triboelectric materials in our daily life.

Coherence between self-reported and objectively measured physical activity in patients with chronic obstructive lung disease: a systematic review

The beneficial effects of physical activity (PA) in patients with COPD, as well as the methods of their assessment, are well known and described. As objective measures of PA, such as the use of motion sensors, video recordings, exercise capacity testing, and indirect calorimetry, are not easily obtained in the daily clinical life, the reliability of the more accessible self-reported measurements of PA is important. In this review, we systematically identified original studies involving COPD patients and at least one parameter of self-reported and objective exercise testing, and analyzed every article for coherence between the objectively and self-reported measured PA. The studies are few, small, and very diverse, both in their use of questionnaires and objective measurements. Self-reported assessments were found to generally overestimate the level of PA compared to measurements made objectively by activity monitors; however, more studies are needed to rely solely on the use of PA questionnaires in COPD patients. The most accurate and valid questionnaires appear to be the self-completed Physical Activity Scale for the Elderly and the interviewer-completed Stanford Seven-Day Physical Activity Recall Questionnaire, but the ideal questionnaire still awaits construction. The motion sensors are accurate and validated in this patient group, especially SenseWear™, but not easily accessible in clinical practice, as they have various technical and adhesive difficulties.

Stair-Walking Performance in Adolescents with Intellectual Disabilities

Most individuals with intellectual disabilities (ID) demonstrate problems in learning and movement coordination. Consequently, they usually have difficulties in activities such as standing, walking, and stair climbing. To monitor the physical impairments of these children, regular gross motor evaluation is crucial. Straight-line level walking is the most frequently used test of their mobility. However, numerous studies have found that unless the children have multiple disabilities, no significant differences can be found between the children with ID and typically-developed children in this test. Stair climbing presents more challenges than level walking because it is associated with numerous physical factors, including lower extremity strength, cardiopulmonary endurance, vision, balance, and fear of falling. Limited ability in those factors is one of the most vital markers for children with ID. In this paper, we propose a sensor-based approach for measuring stair-walking performance, both upstairs and downstairs, for adolescents with ID. Particularly, we address the problem of sensor calibration to ensure measurement accuracy. In total, 62 participants aged 15 to 21 years, namely 32 typically-developed (TD) adolescents, 20 adolescents with ID, and 10 adolescents with multiple disabilities (MD), participated. The experimental results showed that stair-walking is more sensitive than straight-line level walking in capturing gait characteristics for adolescents with ID.

Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones

The growing trend of using smartphones as personal computing platforms to access and store private information has stressed the demand for secure and usable authentication mechanisms. This paper investigates the feasibility and applicability of using motion-sensor behavior data for user authentication on smartphones. For each sample of the passcode, sensory data from motion sensors are analyzed to extract descriptive and intensive features for accurate and fine-grained characterization of users’ passcode-input actions. One-class learning methods are applied to the feature space for performing user authentication. Analyses are conducted using data from 48 participants with 129,621 passcode samples across various operational scenarios and different types of smartphones. Extensive experiments are included to examine the efficacy of the proposed approach, which achieves a false-rejection rate of 6.85% and a false-acceptance rate of 5.01%. Additional experiments on usability with respect to passcode length, sensitivity with respect to training sample size, scalability with respect to number of users, and flexibility with respect to screen size were provided to further explore the effectiveness and practicability. The results suggest that sensory data could provide useful authentication information, and this level of performance approaches sufficiency for two-factor authentication on smartphones. Our dataset is publicly available to facilitate future research.

Commercial Motion Sensor Based Low-Cost and Convenient Interactive Treadmill

Interactive treadmills were developed to improve the simulation of overground walking when compared to conventional treadmills. However, currently available interactive treadmills are expensive and inconvenient, which limits their use. We propose a low-cost and convenient version of the interactive treadmill that does not require expensive equipment and a complicated setup. As a substitute for high-cost sensors, such as motion capture systems, a low-cost motion sensor was used to recognize the subject’s intention for speed changing. Moreover, the sensor enables the subject to make a convenient and safe stop using gesture recognition. For further cost reduction, the novel interactive treadmill was based on an inexpensive treadmill platform and a novel high-level speed control scheme was applied to maximize performance for simulating overground walking. Pilot tests with ten healthy subjects were conducted and results demonstrated that the proposed treadmill achieves similar performance to a typical, costly, interactive treadmill that contains a motion capture system and an instrumented treadmill, while providing a convenient and safe method for stopping.

Fabric-based integrated energy devices for wearable activity monitors

A wearable fabric-based integrated power-supply system that generates energy triboelectrically using human activity and stores the generated energy in an integrated supercapacitor is developed. This system can be utilized as either a self-powered activity monitor or as a power supply for external wearable sensors. These demonstrations give new insights for the research of wearable electronics.

Techniques to measure free-living energy expenditure during pregnancy - A guide for clinicians and researchers

As maternal overweight and obesity increase in prevalence, the need to optimise gestational weight gain has entered the forefront due to the well-documented poor fetal and maternal outcomes associated with excess adipose tissue. Measurements of changes in energy expenditure form crucial components of effective weight management programmes, and they are also important for clarifying the energy requirements of pregnancy. This review evaluates the primary techniques for measuring free-living energy expenditure for use in pregnancy, with special consideration of the dynamic physiological changes that occur in this state. The methods that will be discussed include the doubly labelled water method, activity recall and activity logs, heart rate monitors and motion sensors. This article may be useful for clinicians seeking to implement active gestational weight management by enabling advice on increases in caloric intake linked to measured changes in energy expenditure, and for researchers studying energy metabolism in pregnancy.

Validity of ActiGraph 2-regression model, Matthews cut-points, and NHANES cut-points for assessing free-living physical activity

BACKGROUND

The purpose of this study was to compare the 2006 and 2010 Crouter algorithms for the ActiGraph accelerometer and the NHANES and Matthews cut-points, to indirect calorimetry during a 6-hr free-living measurement period.

METHODS

Twenty-nine participants (mean ± SD; age, 38 ± 11.7 yrs; BMI, 25.0 ± 4.6 kg·m-2) were monitored for 6 hours while at work or during their leisure time. Physical activity (PA) data were collected using an ActiGraph GT1M and energy expenditure (METs) was measured using a Cosmed K4b2. ActiGraph prediction equations were compared with the Cosmed for METs and time spent in sedentary behaviors, light PA (LPA), moderate PA (MPA), and vigorous PA (VPA).

RESULTS

The 2010 Crouter algorithm overestimated time spent in LPA, MPA, and VPA by 9.0%-44.5% and underestimated sedentary time by 20.8%. The NHANES cut-points overestimated sedentary time and LPA by 8.3%-9.9% and underestimated MPA and VPA by 50.4%-56.7%. The Matthews cut-points overestimated sedentary time (9.9%) and MPA (33.4%) and underestimated LPA (25.7%) and VPA (50.1%). The 2006 Crouter algorithm was within 1.8% of measured sedentary time; however, mean errors ranged from 34.4%-163.1% for LPA, MPA, and VPA.

CONCLUSION

Of the ActiGraph prediction methods examined, none of them was clearly superior for estimating free-living PA compared with indirect calorimetry.

Accelerometer use with children, older adults, and adults with functional limitations

Accurately assessing physical activity behavior in children, older adults, and adults with functional limitations is essential to further our understanding of determinants of physical activity behavior in these populations and to design, implement, and evaluate interventions designed to increase physical activity participation. Objective methods to assess physical activity behavior, owing to improvements in accuracy and precision over self-report measures, have become common in research and practice settings. This article reviews the current use of objective methods to assess physical activity in observational, determinant, and intervention studies for children, older adults, and adults with functional limitations. Important considerations are presented when adopting prediction algorithms developed on one population, and using in another population that is markedly different in age, health, and functional status. Best practices are presented, along with future recommendations for research to advance this area of scientific inquiry.

Refined two-regression model for the ActiGraph accelerometer

PURPOSE

The purpose of this study was to refine the 2006 Crouter two-regression model to eliminate the misclassification of walking or running when starting an activity in the middle of a minute on the ActiGraph clock.

METHODS

Forty-eight participants (mean [SD] age = 35 [11.4] yr) performed 10-min bouts of various activities ranging from sedentary behaviors to vigorous physical activity. Eighteen activities were divided into three routines, and 20 participants performed each routine. Participants wore an ActiGraph accelerometer on the hip, and a portable indirect calorimeter was used to measure energy expenditure. Forty-five routines were used to develop the refined two-regression model, and 15 routines were used to cross validate the model. Coefficient of variation (CV) was used to classify each activity as continuous walking or running (CV < or = 10) or intermittent lifestyle activity (CV > 10).

RESULTS

An exponential regression equation and a cubic equation using the natural log of the 10-s counts were developed to predict METs every 10 s for walking or running and intermittent lifestyle activities, respectively. The refined method examines each 10-s epoch and all combinations of the surrounding five 10-s epochs to find the lowest CV. In the cross-validation group, the refined method was not significantly different from measured METs for any activity (P > 0.05), except cycling (P < 0.05). In addition, the 2006 and the refined two-regression models had similar accuracy and precision for estimating energy expenditure during structured activities.

CONCLUSION

The refined two-regression model should eliminate the misclassification of transitional minutes when changing activities that start and stop in the middle of a minute on the ActiGraph clock, thus improving the estimate of free-living energy expenditure.

REMOTE, a Wireless Sensor Network Based System to Monitor Rowing Performance

In this paper, we take a hard look at the performance of REMOTE, a sensor network based application that provides a detailed picture of a boat movement, individual rower performance, or his/her performance compared with other crew members. The application analyzes data gathered with a WSN strategically deployed over a boat to obtain information on the boat and oar movements. Functionalities of REMOTE are compared to those of RowX [1] outdoor instrument, a commercial wired sensor instrument designed for similar purposes. This study demonstrates that with smart geometrical configuration of the sensors, rotation and translation of the oars and boat can be obtained. Three different tests are performed: laboratory calibration allows us to become familiar with the accelerometer readings and validate the theory, ergometer tests which help us to set the acquisition parameters, and on boat tests shows the application potential of this technologies in sports.