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Characterization of Human Postural Balance under Compliance and Deep Learning for Predicting Environmental Conditions during Postural Balance

Description

This thesis work presents two separate studies:The first study assesses standing balance under various 2-dimensional (2D) compliant environments simulated using a dual-axis robotic platform and vision conditions. Directional virtual time-to-contact (VTC) measures were introduced to better characterize postural balance from both temporal and spatial aspects, and enable prediction of fall-relevant directions. Twenty healthy young adults were recruited to perform quiet standing tasks on the platform. Conventional stability measures, namely center-of-pressure (COP) path length and COP area, were also adopted for further comparisons with the proposed VTC. The results indicated that postural balance was adversely impacted, evidenced by significant decreases in VTC and increases in COP path length/area measures, as the ground compliance increased and/or in the absence of vision (ps < 0.001). Interaction effects between environment and vision were observed in VTC and COP path length measures (ps ≤ 0.05), but not COP area (p = 0.103). The estimated likelihood of falls in anterior-posterior (AP) and medio-lateral (ML) directions converged to nearly 50% (almost independent of the foot setting) as the experimental condition became significantly challenging. The second study introduces a deep learning approach using convolutional neural network (CNN) for predicting environments based on instant observations of sway during balance tasks. COP data were collected from fourteen subjects while standing on the 2D compliant environments. Different window sizes for data segmentation were examined to identify its minimal length for reliable prediction. Commonly-used machine learning models were also tested to compare their effectiveness with that of the presented CNN model. The CNN achieved above 94.5% in the overall prediction accuracy even with 2.5-second length data, which cannot be achieved by traditional machine learning models (ps < 0.05). Increasing data length beyond 2.5 seconds slightly improved the accuracy of CNN but substantially increased training time (60% longer). Importantly, averaged normalized confusion matrices revealed that CNN is much more capable of differentiating the mid-level environmental condition. These two studies provide new perspectives in human postural balance, which cannot be interpreted by conventional stability analyses. Outcomes of these studies contribute to the advancement of human interactive robots/devices for fall prevention and rehabilitation.

ContributorsPhan, Vu Nguyen (Author) / Lee, Hyunglae (Thesis advisor) / Peterson, Daniel (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)

Created2021

Tai Chi for Falls Prevention: A Student-Led DNP Project

Description

Aim: To reduce the fear of falling in an elderly population by teaching ‘Tai Chi for Falls Prevention’ classes twice a week for 12 weeks.

Background & Significance: Falls continue to be the leading cause of injury-related deaths of Arizonans who are 65 years or older - well above the national average. It is predicted that by 2030, national medical spending for this population will total over $31 billion, yearly. Tai Chi is revered for being a beneficial form of simple, low-impact exercise, which the CDC endorses for its falls risk reduction benefits.

Methods: The intervention consisted of 60-minute classes occurring twice a week for 12 weeks. Participants were English-speaking, between 65-85 years old, and able to ambulate independently. Appropriate pre-screening tools were used before applicants consented. Their Fear of Falling (FoF) was measured using a fall risk perception tool at the beginning, middle, and the end of the project. This ordinal data was analyzed with Friedman ANOVA using SPSS 25

Outcomes/Results: After enrolling five total participants, only three completed the project. This severely limited data analysis of their FoF, resulting in a statistical significance (p = 0.68), deeming the intervention ineffective - Despite observable downwards trending FoF scores.

Conclusion: The acceptance of the null hypothesis is attributed to the low enrollment and high attrition rate. Also, the only data measured was quantifiable, subjective data. Future projects could add objective data to reinforce the benefits of Tai Chi. This might reinforce the validity of Tai Chi as a practical recommendation due to its cost-effective simple interventional design and effectiveness for prevention of accidental falls. Increased focus on improved recruitment & retainment strategies should be prioritized for similar projects in the future.

ContributorsSawicki, Graham C. (Author) / Thrall, Charlotte (Thesis advisor)

Created2019-04-15

Perturbation-based Training on Compliant Surfaces to Improve Balance in Children with Cerebral Palsy

Description

Children with cerebral palsy suffer from balance deficits that may greatly reduce their quality of life. However, recent advancements in robotics allow for balance rehabilitation paradigms that provide greater control of the training environment and more robust measurement techniques. Previous works have shown functional balance improvement using standing surface perturbations and compliant surface balancing. Visual feedback during balance training has also been shown to improve postural balance control. However, the combined effect of these interventions has not been evaluated. This paper presents a robot-aided rehabilitation study for two children with cerebral palsy on a side-specific performance-adaptive compliant surface with perturbations. Visual feedback of the participant’s center of pressure and weight distribution were used to evaluate successful balance and trigger perturbations after a period of successful balancing. The platform compliance increased relative to the amount of successful balance during each training interval. Participants trained for 6 weeks including 10, less than 2 hours long, training sessions. Improvements in functional balance as assessed by the Pediatric Balance Scale, the Timed 10 Meter Walk Test, and the 5 Times Sit-to-Stand Test were observed for both participants. There was a reduction in fall risk as evidenced by increased Virtual Time to Contact and an increase in dynamic postural balance supported by a faster Time to Perturb, Time to Stabilize, and Percent Stabilized. A mixed improvement in static postural balance was also observed. This paper highlights the efficacy of robot-aided rehabilitation interventions as a method of balance therapy for children with cerebral palsy.

ContributorsPhillips, Connor (Author) / Lee, Hyunglae (Thesis director) / Marvi, Hamidreza (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)

Created2021-12