Inertial Measuring System to Evaluate Gait Parameters and Dynamic Alignments for Lower-Limb Amputation Subjects.

The study aims to construct an inertial measuring system for the application of amputee subjects wearing a prosthesis. A new computation scheme to process inertial data by installing seven wireless inertial sensors on the lower limbs was implemented and validated by comparing it with an optical motion capture system. We applied this system to amputees to verify its performance for gait analysis. The gait parameters are evaluated to objectively assess the amputees' prosthesis-wearing status. The Madgwick algorithm was used in the study to correct the angular velocity deviation using acceleration data and convert it to quaternion. Further, the zero-velocity update method was applied to reconstruct patients' walking trajectories. The combination of computed walking trajectory with pelvic and lower limb joint motion enables sketching the details of motion via a stickman that helps visualize and animate the walk and gait of a test subject. Five participants with above-knee (n = 2) and below-knee (n = 3) amputations were recruited for gait analysis. Kinematic parameters were evaluated during a walking test to assess joint alignment and overall gait characteristics. Our findings support the feasibility of employing simple algorithms to achieve accurate and precise joint angle estimation and gait parameters based on wireless inertial sensor data.

Effectiveness of Elastic Band Exercises on the Functional Fitness of Older Adults in Long-Term Care Facilities.

BACKGROUND: Population aging has caused a rise in the institutionalization, disability, and mortality rates of older adults worldwide. Older adults are able to engage in muscle training. Elastic band exercises can safely and effectively improve the upper and lower muscle strength and balance of older adults. PURPOSE: This study was developed to examine the effects of a 3-month elastic band exercise program on the activities of daily living (ADLs), hand muscle strength, balance, and lower limb muscle strength of older adults living in institutional settings. METHODS: This was a randomized controlled trial. Sixty-one participants were randomly sampled from two long-term care facilities (LTCFs) in northern Taiwan (31 participants in the experimental group and 30 participants in the control group). Both groups underwent pretesting concurrently. The experimental group participated in 3 months of elastic band exercises, whereas the control group participated in the routine exercise program in their LTCFs. All of the participants were tested 1 and 3 months after the intervention. RESULTS: The average ADL, hand muscle strength, balance, and lower limb muscle strength scores of participants in the experimental group had improved significantly more than those of the control group at posttest (all p s < .05). CONCLUSIONS/IMPLICATIONS FOR PRACTICE: Elastic band exercises positively affect ADLs, hand muscle strength, balance, and lower limb muscle strength in older adults living in LTCFs. Moreover, the high benefit-to-cost ratio of these exercises helps lower the threshold of health promotion. We recommend including elastic band exercises in routine activities and designing different elastic band exercises for older adults at different proficiency levels. Furthermore, an elastic band exercise network should be established to improve the policy and implementation aspects of elastic band activities, raise awareness among community-dwelling and institutionalized older adults, and promote elastic band exercises to LTCFs nationwide.

Optimizing the Sensor Placement for Foot Plantar Center of Pressure without Prior Knowledge Using Deep Reinforcement Learning.

We study the foot plantar sensor placement by a deep reinforcement learning algorithm without using any prior knowledge of the foot anatomical area. To apply a reinforcement learning algorithm, we propose a sensor placement environment and reward system that aims to optimize fitting the center of pressure (COP) trajectory during the self-selected speed running task. In this environment, the agent considers placing eight sensors within a 7 × 20 grid coordinate system, and then the final pattern becomes the result of sensor placement. Our results show that this method (1) can generate a sensor placement, which has a low mean square error in fitting ground truth COP trajectory, and (2) robustly discovers the optimal sensor placement in a large number of combinations, which is more than 116 quadrillion. This method is also feasible for solving different tasks, regardless of the self-selected speed running task.