How Long Should GPS Recording Lengths Be to Capture the Community Mobility of An Older Clinical Population? A Parkinson’s Example

Wearable global position system (GPS) technology can help those working with older populations and people living with movement disorders monitor and maintain their mobility level. Health research using GPS often employs inconsistent recording lengths due to the lack of a standard minimum GPS recording length for a clinical context. Our work aimed to recommend a GPS recording length for an older clinical population. Over 14 days, 70 older adults with Parkinson’s disease wore the wireless inertial motion unit with GPS (WIMU-GPS) during waking hours to capture daily “time outside”, “trip count”, “hotspots count” and “area size travelled”. The longest recording length accounting for weekend and weekdays was ≥7 days of ≥800 daily minutes of data (14 participants with 156, 483.9 min recorded). We compared the error rate generated when using data based on recording lengths shorter than this sample. The smallest percentage errors were observed across all outcomes, except “hotspots count”, with daily recordings ≥500 min (8.3 h). Eight recording days will capture mobility variability throughout days of the week. This study adds empirical evidence to the sensor literature on the required minimum duration of GPS recording.

An Accessible, 16-Week Neck Strength Training Program Improves Head Kinematics Following Chest Perturbation in Young Soccer Athletes

Context: Neck size and strength may be associated with head kinematics and concussion risks. However, there is a paucity of research examining neck strengthening and head kinematics in youths. In addition, neck training is likely lacking in youth sport due to a perceived inadequacy of equipment or time. Objective: Examine neck training effects with minimal equipment on neck strength and head kinematics following chest perturbations in youth athletes. Design: Single-group, pretest–posttest case series. Setting: Athlete training center. Participants: Twenty-five (14 men and 11 women) youth soccer athletes (9.8 [1.5] y). Intervention: Sixteen weeks of twice-weekly neck-focused resistance training utilizing bands, body weight, and manual resistance. Main Outcome Measures: Head kinematics (angular range of motion, peak anterior–posterior linear acceleration, and peak resultant linear acceleration) were measured by an inertial motion unit fixed to the apex of the head during torso perturbations. Neck-flexion and extension strength were assessed using weights placed on the forehead and a plate-loaded neck harness, respectively. Neck length and circumference were measured via measuring tape. Results: Neck extension (increase in median values for all: +4.5 kg, +100%, P < .001; females: +4.5 kg, +100%, P = .002; males: +2.2 kg, +36%, P = .003) and flexion (all: +3.6 kg, +114%, P < .001; females: +3.6 kg, +114%, P = .004; males: +3.6 kg, +114%, P = .001) strength increased following the intervention. Men and women both experienced reduced perturbation-induced head pitch (all: −84%, P < .001). However, peak resultant linear acceleration decreased in the female (−53%, P = .004), but not male (−31%, P = 1.0) subgroup. Preintervention peak resultant linear acceleration and extension strength (R2 = .21, P = .033) were the closest-to-significance associations between head kinematics and strength. Conclusions: Young athletes can improve neck strength and reduce perturbation-induced head kinematics following a 16-week neck strengthening program. However, further research is needed to determine the effect of improved strength and head stabilization on concussion injury rates.

Combining Direct and Indirect User Data for Calculating Social Impact Indicators of Products in Developing Countries

Evaluating the social impacts of engineered products is critical to ensuring that products are having their intended positive impacts and learning how to improve product designs for a more positive social impact. Quantitative evaluation of product social impacts is made possible through the use of social impact indicators, which combine the user data in a meaningful way to give insight into the current social condition of an individual or population. Most existing methods for collecting these user data for social impact indicators require direct human interaction with users of a product (e.g., interviews, surveys, and observational studies). These interactions produce high-fidelity data that help indicate the product impact but only at a single snapshot in time and are typically infrequently collected due to the large human resources and cost associated with obtaining them. In this article, a framework is proposed that outlines how low-fidelity data often obtainable using remote sensors, satellites, or digital technology can be collected and correlated with high-fidelity, infrequently collected data to enable continuous, remote monitoring of engineered products via the user data. These user data are critical to determining current social impact indicators that can be used in a posteriori social impact evaluation. We illustrate an application of this framework by demonstrating how it can be used to collect data for calculating several social impact indicators related to water hand pumps in Uganda. Key to this example is the use of a deep learning model to correlate user type (man, woman, or child statured) with the raw hand pump data obtained via an integrated motion unit sensor for 1200 hand pump users.

Application of Inertial Motion Unit-Based Kinematics to Assess the Effect of Boot Modifications on Ski Jump Landings—A Methodological Study

Biomechanical studies of winter sports are challenging due to environmental conditions which cannot be mimicked in a laboratory. In this study, a methodological approach was developed merging 2D video recordings with sensor-based motion capture to investigate ski jump landings. A reference measurement was carried out in a laboratory, and subsequently, the method was exemplified in a field study by assessing the effect of a ski boot modification on landing kinematics. Landings of four expert skiers were filmed under field conditions in the jump plane, and full body kinematics were measured with an inertial motion unit (IMU) -based motion capture suit. This exemplary study revealed that the combination of video and IMU data is viable. However, only one skier was able to make use of the added boot flexibility, likely due to an extended training time with the modified boot. In this case, maximum knee flexion changed by 36° and maximum ankle flexion by 13°, whereas the other three skiers changed only marginally. The results confirm that 2D video merged with IMU data are suitable for jump analyses in winter sports, and that the modified boot will allow for alterations in landing technique provided that enough time for training is given.