Christopher A. Bailey

2. Abstract and key terms: Spatiotemporal variability during gait is linked to fall risk and could be monitored using wearable sensors. Although many users prefer wrist-worn sensors, most applications position at other sites. We developed and evaluated an application using a consumer-grade smartwatch inertial measurement unit (IMU). Young adults (N = 41) completed seven-minute conditions of treadmill gait at three different speeds. Single-stride outcomes (stride time, length, width, and speed) and spatiotemporal variability (coefficient of variation of each single-stride outcome) were recorded using an optoelectronic system, while 232 single- and multi-stride IMU metrics were recorded using an Apple Watch Series 5. These metrics were input to train linear, ridge, support vector machine (SVM), random forest, and extreme gradient boosting (xGB) models of each spatiotemporal outcome. We conducted Model × Condition ANOVAs to explore model sensitivity to speed-related responses. xGB models were best for single-stride outcomes (relative mean absolute error [% error]: 7–11%; intraclass correlation coefficient [ICC _2,1 ]: 0.60–0.86) and SVM models were best for spatiotemporal variability (% error: 18–22%; ICC _2,1 = 0.47–0.64). Spatiotemporal changes with speed were captured by these models (Condition: p < 0.00625). Results support the feasibility of monitoring multi-stride spatiotemporal parameters using a smartwatch IMU and machine learning.

Motor variability in gait is frequently linked to fall risk, yet field-based biomechanical joint evaluations are scarce. We evaluated the validity and sensitivity of an inertial measurement unit (IMU)-driven biomechanical model of joint angle variability for gait. Fourteen healthy young adults completed seven-minute trials of treadmill gait at several speeds and arm swing amplitudes. Trunk, pelvis, and lower-limb joint kinematics were estimated by IMU- and optoelectronic-based models using OpenSim. We calculated range of motion (ROM), magnitude of variability (meanSD), local dynamic stability (λ_max), persistence of ROM fluctuations (DFAα), and regularity (SaEn) of each angle over 200 continuous strides, and evaluated model accuracy (RMSD: root mean square difference), consistency (ICC_2,1: intraclass correlation), biases, limits of agreement, and sensitivity to within-participant gait responses (effects of speed and swing). RMSDs of joint angles were 1.7–9.2° (pooled mean of 4.8°), excluding ankle inversion. ICCs were mostly good to excellent in the primary plane of motion for ROM and in all planes for meanSD and λ_max, but were poor to moderate for DFAα and SaEn. Modelled speed and swing responses for ROM, meanSD, and λ_max were similar. Results suggest that the IMU-driven model is valid and sensitive for field-based assessments of joint angle time series, ROM in the primary plane of motion, magnitude of variability, and local dynamic stability.

Large undergraduate courses make it difficult for students to achieve learning outcomes, in part due to the lack of resources available to course instructors to support student learning in these intimidating and often impersonal settings. One way to support instructor teaching and student learning is the implementation of undergraduate peer mentoring programs, which capitalize on the Students-as-Partners framework. Undergraduate mentors’ relatability to their peers and their mastery of the course content make them excellent resources. This paper describes the development and implementation of a university-wide undergraduate peer mentoring program at McGill University in Canada and its impact on student learning as perceived by three populations: instructors, peer mentors, and students. Data on perceived learning was gathered through qualitative surveys. This case study presents one implementation model that may guide and inform the implementation of similar programs at other higher education institutions.

Motor variability in gait is frequently linked to fall risk, yet field-based biomechanical joint evaluations are scarce. We evaluated the validity and sensitivity of an inertial measurement unit (IMU)-driven biomechanical model of joint angle variability for gait. Fourteen healthy young adults completed seven-minute trials of treadmill gait at several speeds and arm swing amplitudes. Joint kinematics were estimated by IMU- and optoelectronic-based models using OpenSim . We calculated range of motion (ROM), magnitude of variability (meanSD), local dynamic stability (λ _max ), persistence of ROM fluctuations (DFAα), and regularity (SaEn) of each angle over 200 continuous strides, and evaluated model accuracy (e.g., RMSD: root mean square difference), consistency (ICC _2,1 : intraclass correlation), biases, limits of agreement, and sensitivity to within-participant gait responses (effects of Speed and Swing). RMSDs of joint angles were 1.7–7.5° (pooled mean of 4.8°), excluding ankle inversion. ICCs were mostly good– excellent in the primary plane of motion for ROM and in all planes for meanSD and λ _max , but were poor–moderate for DFAα and SaEn. Modeled Speed and Swing responses for ROM, meanSD, and λ _max were similar. Results suggest that the IMU-driven model is valid and sensitive for field-based assessments of joint angles and several motor variability features.

Motor variability is a fundamental feature of gait. Altered arm swing and lower limb asymmetry (LLA) may be contributing factors having been shown to affect the magnitude and dynamics of variability in spatiotemporal and trunk motion. However, the effects on lower limb joints remain unclear. Full-body kinematics of 15 healthy young adults were recorded during treadmill walking using the Computer-Assisted Rehabilitation Environment system. Participants completed six trials, combining three arm swing (AS) amplitude (normal, active, held) and two LLA (symmetrical, asymmetrical) conditions. The mean standard deviation (meanSD), maximum Lyapunov exponent (λ _max ), detrended fluctuation analysis scaling exponent of range of motion (DFAα), and sample entropy (SaEn) were computed for tridimensional trunk, pelvis, and lower limb joint angles, and compared using repeated-measures ANOVAs. Relative to normal AS, active AS increased meanSD of all joint angles, λ _max of frontal plane hip and ankle angles, and SaEn of sagittal plane ankle angles. Active AS, however, did not affect λ _max or SaEn of trunk or pelvis angles. LLA increased meanSD of sagittal plane joint angles, λ _max of Euclidean norm trunk angle and of lower limb joint angles, and SaEn of ankle dorsiflexion/ plantarflexion, but decreased SaEn of tridimensional trunk angles and hip rotation in the slower moving leg. Alterations in lower limb variability with active AS and LLA suggest that young adults actively exploit their lower limb redundancies to maintain gait. This appears to preserve trunk stability and regularity during active AS but not during LLA.

Aging affects fatigability and is a risk factor for incurring a fatigue-related injury in the neck/shoulder region. Age-related changes in the electromyographical features of motor control may be partly responsible. Young (N = 17) and older (N = 13) adults completed a reach-and-lift task at their self-selected speed, before and after a fatiguing task targeting the neck/shoulder. Electromyography amplitude (root mean square), amplitude variability (root mean square coefficient of variation [CV]), functional connectivity (normalized mutual information [NMI]), and functional connectivity variability (NMI CV) were extracted from several muscles and analyzed for effects and interactions of age using general estimating equation models. Root mean square CV and deltoid NMI CV increased from pre- to postfatigue (ps < .05). Upper trapezius–deltoid NMI decreased for young, but increased for older adults, while the opposite response was found for lower trapezius–deltoid NMI (ps < .05). Older adults seem to adapt to fatigue in reach-and-lift movement with a cranial shift in control of the scapula.

1) Determine the effects of old age on sensorimotor responses to a fatiguing work-like task. 2) Explore how old age influences the relationships between task fatigability, everyday perceptions of fatigability, and sensorimotor function. Healthy young (N = 17, 9W) and older (N = 13, 10W) adults completed the Pittsburgh Fatigability Scale to assess everyday perceptions of physical (PF) and mental fatigability and performed a repetitive tapping task to fatigue. Before and after the task, grip strength was assessed using a hand-grip dynamometer and touch-pressure sensitivity was measured (shoulder, hand) using Semmes-Weinstein monofilaments. Older, but not young adults, had increased touch-pressure sensitivity at the shoulder after fatigue (interaction, p = 0.007). No changes in grip strength were observed (p>0.05). Task fatigability was not different between young and old adults (p>0.05). Having less task fatigability was associated with lower PF, higher grip strength, and higher touch-pressure sensitivity at the hand (ρ = 0.37–0.58, p<0.05), with the hand sensation association also observed in the old adult subgroup (ρ = 0.56, p = 0.046). With old age, there were fatigue-related alterations to sensory but not physical function. While task fatigability was associated with perceptual, physical, and sensory features, sensory features appear to have a more important role with old age.