A Wearable System for In-Home and Long-Term Assessment of Fetal Movement

Objectives: This paper presents a novel wearable system for in-home and long-term fetal movement monitoring on a reliable and easily accessible basis.Material and methods: The system mainly consists of four accelerometers for fetal movement signal acquisition, a microcontroller for signal processing and an Android-based device interacting with the microcontroller via Bluetooth Low Energy (BLE), providing the mother with information related to the fetal movement in an intelligible way.Results: The proposed system can deliver reliable results with a specificity of 0.99 and a sensitivity of 0.77 for fetal movement time series signal classification.Conclusion: The proposed wearable system will provide a good alternative to optimize the use of medical professionals and hospital resources, and has potential applications in the field of e-Health home care. Besides, the fetal movement acceleration signals acquired with volunteers (pregnant women) help establish an initial database for future medical analysis of sensor-recorded fetal behaviors.     

Concurrent validity of the Microsoft Kinect for assessment of spatiotemporal gait variables

Spatiotemporal characteristics of gait such as step time and length are often associated with overall physical function in clinical populations, but can be difficult, time consuming and obtrusive to measure. This study assessed the concurrent validity of overground walking spatiotemporal data recorded using a criterion reference – a marker-based three-dimensional motion analysis (3DMA) system – and a low-cost, markerless alternative, the automated skeleton tracking output from the Microsoft Kinect™ (Kinect). Twenty-one healthy adults performed normal walking trials while being monitored using both systems. The outcome measures of gait speed, step length and time, stride length and time and peak foot swing velocity were derived using supervised automated analysis. To assess the agreement between the Kinect and 3DMA devices, Bland–Altman 95% bias and limits of agreement, percentage error, relative agreement (Pearson's correlation coefficients: r) overall agreement (concordance correlation coefficients: r_c) and landmark location linearity as a function of distance from the sensor were determined. Gait speed, step length and stride length from the two devices possessed excellent agreement (r and r_c values >0.90). Foot swing velocity possessed excellent relative (r=0.93) but only modest overall (r_c=0.54) agreement. Step time (r=0.82 and r_c=0.23) and stride time (r=0.69 and r_c=0.14) possessed excellent and modest relative agreement respectively but poor overall agreement. Landmark location linearity was excellent (R²=0.991). This widely available, low-cost and portable system could provide clinicians with significant advantages for assessing some spatiotemporal gait parameters. However, caution must be taken when choosing outcome variables as some commonly reported variables cannot be accurately measured.     

Optimisation of energetic and reproductive gains explains behavioural responses to environmental variation across seasons and years

Animals switch between inactive and active states, simultaneously impacting their energy intake, energy expenditure and predation risk, and collectively defining how they engage with environmental variation and trophic interactions. We assess daily activity responses to long‐term variation in temperature, resources and mating opportunities to examine whether individuals choose to be active or inactive according to an optimisation of the relative energetic and reproductive gains each state offers. We show that this simplified behavioural decision approach predicts most activity variation (R² = 0.83) expressed by free‐ranging red squirrels over 4 years, as quantified through accelerometer recordings (489 deployments; 5066 squirrel‐days). Recognising activity as a determinant of energetic status, the predictability of activity variation aggregated at a daily scale, and the clear signal that behaviour is environmentally forced through optimisation of gain, provides an integrated approach to examine behavioural variation as an intermediary between environmental variation and energetic, life‐history and ecological outcomes.     

Subject-specific and group-based running pattern classification using a single wearable sensor

The objective of this study was to determine whether subject-specific or group-based models provided better classification accuracy to identify changes in biomechanical running gait patterns across different inclination conditions. The classification process was based on measurements from a single wearable sensor using a total of 41,780 strides from eleven recreational runners while running in real-world and uncontrolled environment. Biomechanical variables included pelvic drop, ground contact time, braking, vertical oscillation of pelvis, pelvic rotation, and cadence were recorded during running on three inclination grades: downhill, −2° to −7°; level, −0.2° to +0.2°; and uphill, +2° to +7°. An ensemble and non-linear machine learning algorithm, random forest (RF), was used to classify inclination condition and determine the importance of each of the biomechanical variables. Classification accuracy was determined for subject-specific and group-based RF models. The mean classification accuracy of all subject-specific RF models was 86.29%, while group-based classification accuracy was 76.17%. Braking was identified as the most important variable for all the runners using the group-based model and for most of the runners based on a subject-specific models. In addition, individual runners used different strategies across different inclination conditions and the ranked order of variable importance was unique for each runner. These results demonstrate that subject-specific models can better characterize changes in gait biomechanical patterns compared to a more traditional group-based approach.     

Use of audio biofeedback to reduce tibial impact accelerations during running

Visual biofeedback of tibial peak positive acceleration (PPA) during running has been used successfully as a method of gait retraining to reduce PPAs. Audio biofeedback generated from PPA may present a novel, portable alternative. The purpose of this study was to investigate the feasibility of using PPA-generated audio biofeedback to reduce PPAs while running. Nine runners were fitted with a wireless accelerometer on their left tibia. PPAs were recorded and a custom LabVIEW program was used to emit a single beep once the PPA reached a preset threshold. The numerical difference between this threshold and peak PPA during running was scaled to the pitch of the beep, such that a foot strike with greater PPA would result in a beep with higher pitch. Subjects were then instructed to (1) run without any beeps, and/or (2) keep the pitch of the beep as low as possible. Subjects participated in a single testing session that included a five minute warm-up and two rounds of biofeedback, which consisted of five minutes of running with biofeedback followed by five minutes of running without biofeedback. Subjects were able to significantly reduce PPAs during exposure to audio biofeedback. In addition, two rounds of biofeedback were sufficient for subjects to retain a reduction in PPAs without biofeedback. PPA-generated audio biofeedback therefore appears to be a feasible method of gait retraining to reduce PPAs in runners.     

Classification accuracy of a single tri-axial accelerometer for training background and experience level in runners

Accelerometers are increasingly used tools for gait analysis, but there remains a lack of research on their application to running and their ability to classify running patterns. The purpose of this study was to conduct an exploratory examination into the capability of a tri-axial accelerometer to classify runners of different training backgrounds and experience levels, according to their 3-dimensional (3D) accelerometer data patterns. Training background was examined with 14 competitive soccer players and 12 experienced marathon runners, and experience level was examined with 16 first-time and the same 12 experienced marathon runners. Discrete variables were extracted from 3D accelerations during a short run using root mean square, wavelet transformation, and autocorrelation procedures. A principal component analysis (PCA) was conducted on all variables, including gait speed to account for covariance. Eight PCs were retained, explaining 88% of the variance in the data. A stepwise discriminant analysis of PCs was used to determine the binary classification accuracy for training background and experience level, with and without the PC of Speed. With Speed, the accelerometer correctly classified 96% of runners for both training background and experience level. Without Speed, the accelerometer correctly classified 85% of runners based on training background, but only 68% based on experience level. These findings suggest that the accelerometer is effective in classifying athletes of different training backgrounds, but is less effective for classifying runners of different experience levels where gait speed is the primary discriminator.     

Monitoring external load in elite male handball players depending on playing positions

Monitoring workload is critical for elite training and competition, as well as preventing potential sports injuries. The assessment of external load in team sports has been provided with new technologies that help coaches to individualize training and optimize their team’s playing system. In this study we characterized the physical demands of an elite handball team during an entire sports season. Novel data are reported for each playing position of this highly strenuous body-contact team sport. Sixteen world top players (5 wings, 2 centre backs, 6 backs, 3 line players) were equipped with a local positioning system (WIMU PRO) during fourteen official Spanish first league matches. Playing time, total distance covered at different running speeds, and acceleration variables were monitored. During a handball match, wings cover the greater distance by high-speed running (> 5.0 m·s^-1): 410.3 ± 193.2 m, and by sprint (> 6.7 m·s^-1): 98.0 ± 75.4 m. Centre backs perform the following playing position that supports the highest speed intensities during the matches: high-speed running: 243.2 ± 130.2 m; sprint: 62.0 ± 54.2 m. Centre backs also register the largest number of high-intensity decelerations (n = 142.7 ± 59.5) compared to wings (n = 112.9 ± 56.0), backs (n = 105.2 ± 49.2) and line players: 99.6 ± 28.9). This study provides helpful information for professional coaches and their technical staff to optimize training load and individualize the physical demands of their elite male handball players depending on each playing position.     

Effect of exercise training on total daily physical activity in elderly humans

This study examined the effect of 12 weeks of exercise training on daily physical activity in elderly humans. Training consisted of a weekly group session and an individual session with cardio- and weight-stack machines. A group of 15 subjects served as the exercise group [EXER mean age 59 (SD 4) years], and 7 subjects as the controls [CONT mean age 57 (SD 3) years]. Physical activity and physical fitness were measured before the start of training (T), at week 6 and week 12 (T0, T6, T12 respectively) in EXER, and at T0 and T12 in CONT. Physical activity over 14 days was measured using a tri-axial accelerometer and physical fitness was measured during an incremental exercise test. At T12, mean maximal power output had significantly increased in EXER compared to CONT 8 (SD 12) vs -5 (SD 9) W; P < 0.02] and mean submaximal heart rate (at 100 W) had reduced [-10 (SD 7) vs -2 (SD 6) beats x min(-1); P < 0.05]. No differences or changes in physical activity were observed between EXER and CONT. At T6, physical activity on training days was significantly higher than on non-training days (P < 0.001). When the accelerometer output of the training session was subtracted from the accelerometer output on training days, at T12 non-training physical activity was significantly lower than on non-training days (P < 0.004). Accelerometer output of the individual training session at T12 had significantly increased compared to T6 (P < 0.05), whereas, accelerometer output of the group training session had remained unchanged. In conclusion, in elderly subjects an exercise training programme of moderate intensity resulted in an improved physical fitness but had no effect on total daily physical activity. Training activity was compensated for by a decrease in non-training physical activity.     

Development of an automated method of detecting stereotyped feeding events in multisensor data from tagged rorqual whales

The introduction of animal‐borne, multisensor tags has opened up many opportunities for ecological research, making previously inaccessible species and behaviors observable. The advancement of tag technology and the increasingly widespread use of bio‐logging tags are leading to large volumes of sometimes extremely detailed data. With the increasing quantity and duration of tag deployments, a set of tools needs to be developed to aid in facilitating and standardizing the analysis of movement sensor data. Here, we developed an observation‐based decision tree method to detect feeding events in data from multisensor movement tags attached to fin whales (Balaenoptera physalus). Fin whales exhibit an energetically costly and kinematically complex foraging behavior called lunge feeding, an intermittent ram filtration mechanism. Using this automated system, we identified feeding lunges in 19 fin whales tagged with multisensor tags, during a total of over 100 h of continuously sampled data. Using movement sensor and hydrophone data, the automated lunge detector correctly identified an average of 92.8% of all lunges, with a false‐positive rate of 9.5%. The strong performance of our automated feeding detector demonstrates an effective, straightforward method of activity identification in animal‐borne movement tag data. Our method employs a detection algorithm that utilizes a hierarchy of simple thresholds based on knowledge of observed features of feeding behavior, a technique that is readily modifiable to fit a variety of species and behaviors. Using automated methods to detect behavioral events in tag records will significantly decrease data analysis time and aid in standardizing analysis methods, crucial objectives with the rapidly increasing quantity and variety of on‐animal tag data. Furthermore, our results have implications for next‐generation tag design, especially long‐term tags that can be outfitted with on‐board processing algorithms that automatically detect kinematic events and transmit ethograms via acoustic or satellite telemetry.