Joseph Michael Mahoney

This study evaluated how running with a stroller influences biomechanical parameters commonly associated with injury risk. Specifically, the study investigated changes in vertical and torsional loading and tibial acceleration in runners pushing a jogging stroller compared to running without one. Thirty-eight healthy adult runners participated in trials where they ran over a force plate with and without a stroller. Key measurements included vertical and anterior-posterior ground reaction forces, tibial accelerations, and free moments. The findings demonstrated significant reductions in vertical loading metrics, including vertical impact peak, vertical instantaneous and average loading rates, and vertical impulse, by 8–17% when running with a stroller. These reductions suggest a decreased risk of overuse injuries commonly associated with vertical forces. Conversely, torsional loading parameters, such as peak free moment and free moment impulse, increased significantly, with some measures rising by more than 400%. This increase in torsional loading indicates an elevated risk of stress-related injuries, particularly tibial stress fractures. In addition, tibial acceleration decreased slightly, though to a lesser extent than vertical loading metrics. These results highlight a biomechanical tradeoff when running with a stroller. While the reduction in vertical loading may mitigate the risk of bone stress and overuse injuries, the simultaneous increase in torsional loading could heighten the likelihood of torsional stress injuries. The study underscores the need for further research to explore mitigation strategies, such as optimized stroller designs or alternative pushing techniques, to balance these risks. The findings contribute valuable insights for runners, coaches, and stroller manufacturers aiming to promote safer running practices for caregivers using jogging strollers.

Motion capture systems are ubiquitous in the gait analysis field. They are an excellent way to collect quantitative kinematic and kinetic motion data in an unobtrusive way. However, the cost for even a simple system is prohibitive to small research programs or clinics. Additionally, the required space, amount of equipment and set up time make them difficult to house permanently let alone move to different locations such as a clinic or the field. Here, we present a hardware and software solution to capture a more limited scope of kinematic data, specifically joint angles. The setup requires only one off-the-shelf camera and all data processing is done offline on a standard desktop or laptop. For this version, a treadmill was used to capture multiple strides. It required ten feet of clearance on the side for the camera and the hardware cost was approximately $300. The system is validated against an electrogoniometer and joint angles are found to be, on average, within 0.5 ^° . The system is then employed in a small-scale case study of tracking knee and ankle angles for subjects walking with Ankle Foot Orthoses. We are able to detect clear differences in the ankle range of motion when comparing with-AFO to without. The knee and ankle joint profiles agree qualitatively with the literature.

The knee flexion angle is an important measurement for studies of the human gait. Running is a common activity with a high risk of knee injury. Studying the running gait in realistic situations is challenging because accurate joint angle measurements typically come from optical motion-capture systems constrained to laboratory settings. This study considers the use of shank and thigh inertial sensors within three different filtering algorithms to estimate the knee flexion angle for running without requiring sensor-to-segment mounting assumptions, body measurements, specific calibration poses, or magnetometers. The objective of this study is to determine the knee flexion angle within running applications using accelerometer and gyroscope information only. Data were collected for a single test participant (21-year-old female) at four different treadmill speeds and used to validate the estimation results for three filter variations with respect to a Vicon optical motion-capture system. The knee flexion angle filtering algorithms resulted in root-mean-square errors of approximately three degrees. The results of this study indicate estimation results that are within acceptable limits of five degrees for clinical gait analysis. Specifically, a complementary filter approach is effective for knee flexion angle estimation in running applications.

This paper details the design, construction, and validation of a virtual air hockey table intended as a low-cost motion capture solution with potential entertainment and research applications through reliance on open-source software (e.g., Linux, Blender, GIMP, and Panda3D) and off-the-shelf hardware (e.g., rotary potentiometers, Arduino microcontroller, and LCD projector). In its current configuration, a subject attempts to hit a virtual puck onto a predetermined goal by means of a physical mallet comprised of a three-bar mechanism with potentiometers at its joints. The mallet-puck collision detection is performed in near-real time and the puck's initial velocity is calculated using finite difference method. Puck motion, including wall and obstacle collisions, is calculated using the Euler method. An example use case is provided wherein the device was used for a motor control study of inter-trial variation in skilled task performance.

Daily fantasy sports (DFS) are weekly or daily online contests where real-game performances of individual players are converted to fantasy points (FPTS). Users select players for their lineup to maximize their FPTS within a set player salary cap. This paper focuses on (1) the development of a method to forecast NFL player performance under uncertainty and (2) determining an optimal lineup to maximize FPTS under a set salary limit. A supervised learning neural network was created and used to project FPTS based on past player performance (2018 NFL regular season for this work) prior to the upcoming week. These projected FPTS were used in a mixed integer linear program to find the optimal lineup. The performance of resultant lineups was compared to randomly-created lineups. On average, the optimal lineups outperformed the random lineups. The generated lineups were then compared to real-world lineups from users on DraftKings. The generated lineups generally fell in approximately the 31st percentile (median). The FPTS methods and predictions presented here can be further improved using this study as a baseline comparison.

Purpose: Physical inactivity is a well-known problem in the United States, especially among the younger population. Pokémon Go (PG) is a mobile game that has players catch Pokémon while walking in the real world. The purpose of this study was to assess the effect of playing PG on student physical activity.Methods: Overall, 27 healthy undergraduate students participated in a 2-week study. They were randomly assigned to either PG-playing group or control group. Fitbit Charge HR devices were deployed for 2 weeks to all participants. The daily number of steps and distance travelled were recorded and the groups were compared by using the bootstrapped t-test.Results: No statistically significant difference in means was observed when comparing the average daily step count (p = 0.845) or the average daily distance travelled (p = 0.528) between control and PG-playing participants. Furthermore, the mean difference was close to 0 for each comparison, with 95% confidence intervals of –2072 to 2438 steps and –0.609 to 1.31 km, respectively.Conclusions: PG did not lead to a statistically observable increase in the number of steps or distance travelled by university students. These results may provide an impetus to create alternative interventions with better user engagement by applying mobile health apps with the intent to reach low-activity populations.

With the rising popularity of activity tracking, there is a desire to not only count the number of steps a person takes, but also identify the type of step (e.g., walking or running) they are taking. For rehabilitation and athletic training, this difference is important to the prescribed regiment. Fourteen healthy adults walked, jogged and ran on a treadmill at three different constant speeds (1.21, 2.01, 2.68 m/s) for 90 s. An inertial measurement unit (IMU) with accelerometer and gyroscope was affixed to their left ankle. Collected acceleration and angular velocity data were partitioned into individual time-normalised strides. These data were used as features in the artificial neural network (ANN) that classified the type of stride. Several ANN models were tested: using only acceleration, only angular velocity and both. Using primarily acceleration data in the trained ANN yielded the best results (>94% correct stride-type identification) after cross-validation. The ANN models were able to accurately classify the gait type of each stride using a single wearable IMU. The accuracy of the method should improve further as more data is added to the ANN training.

Background: Reliable step counting is a critical part of locomotion research. Current counting methods can be inaccurate, time consuming, expensive or encumbering to the subject. Here, we present a camera-based optical method for automatically counting steps. Methods: Fifteen healthy adults walked, jogged and ran on a treadmill at three different constant speeds (1.21, 2.01, 2.68 m/s) and once at varying speed (1.21–2.68 m/s) for 90 s. Subjects had visual marker affixed to their left foot while walking. Video was recorded synchronously at low- and high-resolution during trials. The step count found manually from the video was compared to an automated video analysis system using the two configurations of the optical system. Results: Bland–Altman plots, Intra-class correlation coefficients (ICC) and relative error comparison were used for quantitative assessment of device reliability. Reliability of optical method was high (ICC ≥0.98). Conclusions: The method produces accurate step count results for the range of speeds tested. They use customisable open-source software and off-the-shelf hardware. The method has a low cost of implementation compared to many consumer products and grants researchers access to the raw sensor data.