Self-selection of gestational lumbopelvic posture and bipedal evolution

Enhancing fall risk assessment: instrumenting vision with deep learning during walks

BACKGROUND

Falls are common in a range of clinical cohorts, where routine risk assessment often comprises subjective visual observation only. Typically, observational assessment involves evaluation of an individual's gait during scripted walking protocols within a lab to identify deficits that potentially increase fall risk, but subtle deficits may not be (readily) observable. Therefore, objective approaches (e.g., inertial measurement units, IMUs) are useful for quantifying high resolution gait characteristics, enabling more informed fall risk assessment by capturing subtle deficits. However, IMU-based gait instrumentation alone is limited, failing to consider participant behaviour and details within the environment (e.g., obstacles). Video-based eye-tracking glasses may provide additional insight to fall risk, clarifying how people traverse environments based on head and eye movements. Recording head and eye movements can provide insights into how the allocation of visual attention to environmental stimuli influences successful navigation around obstacles. Yet, manual review of video data to evaluate head and eye movements is time-consuming and subjective. An automated approach is needed but none currently exists. This paper proposes a deep learning-based object detection algorithm (VARFA) to instrument vision and video data during walks, complementing instrumented gait.

METHOD

The approach automatically labels video data captured in a gait lab to assess visual attention and details of the environment. The proposed algorithm uses a YoloV8 model trained on with a novel lab-based dataset.

RESULTS

VARFA achieved excellent evaluation metrics (0.93 mAP50), identifying, and localizing static objects (e.g., obstacles in the walking path) with an average accuracy of 93%. Similarly, a U-NET based track/path segmentation model achieved good metrics (IoU 0.82), suggesting that the predicted tracks (i.e., walking paths) align closely with the actual track, with an overlap of 82%. Notably, both models achieved these metrics while processing at real-time speeds, demonstrating efficiency and effectiveness for pragmatic applications.

CONCLUSION

The instrumented approach improves the efficiency and accuracy of fall risk assessment by evaluating the visual allocation of attention (i.e., information about when and where a person is attending) during navigation, improving the breadth of instrumentation in this area. Use of VARFA to instrument vision could be used to better inform fall risk assessment by providing behaviour and context data to complement instrumented e.g., IMU data during gait tasks. That may have notable (e.g., personalized) rehabilitation implications across a wide range of clinical cohorts where poor gait and increased fall risk are common.

Sensory integration and segmental control of posture during pregnancy

BACKGROUND

Approximately 25% of pregnant people fall, yet the underlying mechanisms of this increased fall-risk remain unclear. Prior studies examining pregnancy and balance have utilized center of pressure analyses and reported mixed results. The purpose of this study was to examine sensory and segmental contributions to postural control throughout pregnancy using accelerometer-based measures of sway.

METHODS

Thirty pregnant people (first trimester: n = 10, second trimester: n = 10, third trimester: n = 10) and 10 healthy, nonpregnant control people stood quietly for one minute in four conditions: eyes open on a firm surface, eyes closed on a firm surface, eyes open on a foam pad, and eyes closed on foam. Postural sway was quantified using the root mean square accelerations in the anterior-posterior and medial-lateral directions from an inertial sensor at the lumbar region. Sensory sway ratios, segmental coherence and co-phase, were calculated to assess sensory contributions and segmental control, respectively.

FINDINGS

Pregnant people did not display greater sway compared to healthy, nonpregnant controls. There were no group differences in vestibular, visual, or somatosensory sway ratios, and no significant differences in balance control strategies between pregnant and nonpregnant participants across sensory conditions.

INTERPRETATION

The small effects observed here contrast prior studies and suggest larger, definitive studies are needed to assess the effect of pregnancy on postural control. This study serves as a preliminary exploration of pregnant sensory and segmental postural control and highlights the need for future to hone the role of balance in fall risk during pregnancy.

Different neurocognitive controls modulate obstacle avoidance through pregnancy

Understanding why falls during pregnancy occur at over 25% rate over gestation has clinical impacts on the health of pregnant individuals. Attention, proprioception, and perception of the environment are required to prevent trips and falls. This research aimed to understand how the changes to these neurocognitive processes control obstacle avoidance through gestation. Seventeen pregnant participants were tested five times in 6-week intervals. Participants walked an obstacle course (OC), and we analyzed the crossings over obstacles that were set to 10% of participants' body height. Participants also performed an attentional network test (ANT: performance of specific components of attention), an obstacle perception task (OP: ability to visually define an obstacle and translate that to a body posture), and a joint position sense task (JPS: ability to recognize and recreate a joint position from somatosensation). In the OC task, average leading and trailing foot crossing heights significantly reduced by 13% and 23% respectively, with no change in variation, between weeks 13 and 31 of pregnancy, indicating an increased risk of obstacle contact during this time. The variability in minimum leading foot distances from the obstacle was correlated with all three neurocognition tasks (ANT, OP, and JPS). Increased fall rates in the second and third trimesters of pregnancy may be driven by changes in attention, with additional contributions of joint position sense and environmental perception at various stages of gestation. The results imply that a holistic examination on an individual basis may be required to determine individual trip risk and appropriate safety modifications.

Upper extremity kinematics during walking gait changes through pregnancy

BACKGROUND

Thirty percent of adults in the United States use wearable fitness devices as of 2020 [1], such as fitness watches, to monitor and track health and physical activity parameters. Physical changes during pregnancy may impact wrist worn device accuracy. The arms may be needed as compensation during walking because thorax axial rotation may be inhibited by pelvic tilt during pregnancy [2].

METHODS

To examine arm motion changes, twenty-three pregnant women (28 ± 4 y) were tested in four-week intervals ( ± 2 weeks) at 18-, 22-, 26-, 30- and 34-weeks' gestation. Kinematic data were measured during self-selected speed walking. Segment angles and angular velocities were analyzed over time. Linear regressions were used to analyze the correlations between arm motion and the other kinematic variables.

RESULTS

Arm range of motion significantly increased (p = 0.006) over gestation, but leg, thorax, and pelvis range of motions did not significantly change. Arm range of motion was correlated with pelvis (r² =0.311, p = 0.001, β = 1.724) and leg (r² = 0.285, p = 0.004, β = 1.520) range of motion and gait velocity (r² =0.566, p = 0.001, β = 39.110). Arm velocities significantly increased (p < 0.012), as did leg velocities (p < 0.022) over gestation time, but thorax and pelvis rotational velocities did not significantly change over time. Arm velocity was correlated with leg velocity in both flexion (r² =0.598, p = 0.001, β = 1.61) and extension (r² =0.568, p = 0.001, β = 1.35).

SIGNIFICANCE

Arm swing increases over the course of gestation during walking, which does not follow the exact pattern of changes seen in the legs, thorax, and pelvis. These results show that a typical gait analysis of lower body motions may miss important biomechanical changes or compensations at different points over pregnancy. Future studies should examine why these changes may occur. Studies should also be conducted to see if arm changes impact outcome parameters from fitness watches and affect their validity as an exercise tracker during pregnancy.

How Does the Lumbopelvic Complex Cope with the Obstetrical Load during Standing? Ergonomic Aspects of Body Posture in Pregnant Women

Pregnancy induces numerous modifications in the musculoskeletal system of the female body. Since one of the essential roles of the lumbopelvic structure is to support mechanical loads in the upright position, this study was designed to simulate the response of this complex to the growing foetus in pregnant women. The authors hypothesized that posture (i.e., lordosis and muscle involvement) under pregnancy conditions might be adjusted to minimize the demands of the obstetrical load. The analysis of the load on the musculoskeletal system during gestation was made based on numerical simulations carried out in the AnyBody Modeling System. The pregnancy-related adjustments such as increased pelvic anteversion and increased lumbar lordosis enhance the reduction of muscle activation (e.g., erector spinae, transversus abdominis or iliopsoas), muscle fatigue and spinal load (reaction force). The results may help develop antenatal exercise programs targeting core strength and pelvic stability.