Long-term effects of deep-learning digital therapeutics on pain, movement control, and preliminary cost-effectiveness in low back pain: A randomized controlled trial.

Objective: The present study aimed to compare the effects of a deep learning-based digital application with digital application physical therapy (DPT) and those of conventional physical therapy (CPT) on back pain intensity, limited functional ability, lower extremity weakness, radicular symptoms, limited range of motion (ROM), functional movement, quality of life, cost-effectiveness, and postintervention questionnaires for perceived transmission risk of COVID-19 and satisfaction results in 100 participants with low back pain (LBP). Methods: One hundred participants with LBP were randomized into either DPT or CPT groups, three times per week over four weeks. Outcome measures included the (1) Oswestry Disability Index, (2) Quebec Back Pain Disability Scale, (3) Roland-Morris Disability Questionnaire (RMDQ), (4) Numeric Pain Rating Scale, (5) functional movement screen (FMS), (6) short form-12, (7) lower extremity strength, (8) ROM of trunk flexion, extension, and bilateral side bending, (9) questionnaires for perceived transmission risk of COVID-19, (10) preliminary cost-effectiveness, and (11) postintervention satisfaction questionnaire results. The analysis of variance was conducted at p < 0.05. Results: Analysis of variance showed that DPT showed superior effects, compared to CPT on RMDQ, hip extensor strength, transmission risk of COVID-19, as well as satisfaction. Both groups showed significant improvement pre- and postintervention, suggesting that DPT is as effective as CPT, and was superior in preliminary cost-effectiveness and transmission risk of COVID-19. Conclusions: Our results provide novel, promising clinical evidence that DPT was as effective as CPT in improving structural and functional impairment, activity limitation, and participation restriction. Our results highlight the successful incorporation of DPT intervention for clinical outcome measures, lower extremity strength, trunk mobility, ADL improvement, QOL improvement, and FMS in LBP.

Spine-like Joint Link Mechanism to Design Wearable Assistive Devices.

When we develop wearable assistive devices, comfort and support are two main issues that need to be considered. In conventional design approaches, the degree of freedom of the wearer's joint movements tends to be oversimplified. Accordingly, the wearer's motion becomes restrained and bone/ligament injuries might occur in case of an unexpected fall. To mitigate these issues, this paper proposes a novel joint link mechanism inspired by a human spine structure as well as functionalities. The key feature of the proposed spine-like joint link mechanism is that hemispherical blocks are concatenated via flexible synthetic fiber lines so that their concatenation stiffness can be adjusted according to a tensile force. This feature has a great potentiality for designing a wearable assistive device that can support aged people's sit-to-stand action or augment spinal motion by regulating the concatenation stiffness. In addition, the concatenated hemispherical blocks enable the wearer to move his/her joint with full freedom, which in turn increases the wearer's mobility and prevents joint misalignment. The experimental results with a testbed and a pilot wearer substantiated that the spine-like joint link mechanism can serve as a key component in the design of wearable assistive devices for better mobility.

Hand Gesture Recognition Using EGaIn-Silicone Soft Sensors.

Exploiting hand gestures for non-verbal communication has extraordinary potential in HCI. A data glove is an apparatus widely used to recognize hand gestures. To improve the functionality of the data glove, a highly stretchable and reliable signal-to-noise ratio sensor is indispensable. To do this, the study focused on the development of soft silicone microchannel sensors using a Eutectic Gallium-Indium (EGaIn) liquid metal alloy and a hand gesture recognition system via the proposed data glove using the soft sensor. The EGaIn-silicone sensor was uniquely designed to include two sensing channels to monitor the finger joint movements and to facilitate the EGaIn alloy injection into the meander-type microchannels. We recruited 15 participants to collect hand gesture dataset investigating 12 static hand gestures. The dataset was exploited to estimate the performance of the proposed data glove in hand gesture recognition. Additionally, six traditional classification algorithms were studied. From the results, a random forest shows the highest classification accuracy of 97.3% and a linear discriminant analysis shows the lowest accuracy of 87.4%. The non-linearity of the proposed sensor deteriorated the accuracy of LDA, however, the other classifiers adequately overcame it and performed high accuracies (>90%).

Shared and Task-Specific Muscle Synergies during Normal Walking and Slipping.

Falling accidents are costly due to their prevalence in the workplace. Slipping has been known to be the main cause of falling. Understanding the motor response used to regain balance after slipping is crucial to developing intervention strategies for effective recovery. Interestingly, studies on spinalized animals and studies on animals subjected to electrical microstimulation have provided major evidence that the Central Nervous System (CNS) uses motor primitives, such as muscle synergies, to control motor tasks. Muscle synergies are thought to be a critical mechanism used by the CNS to control complex motor tasks by reducing the dimensional complexity of the system. Even though synergies have demonstrated potential for indicating how the body responds to balance perturbations by accounting for majority of the data set's variability, this concept has not been applied to slipping. To address this gap, data from 11 healthy young adults were collected and analyzed during both unperturbed walking and slipping. Applying an iterative non-negative matrix decomposition technique, four muscle synergies and the corresponding time-series activation coefficients were extracted. The synergies and the activation coefficients were then compared between baseline walking and slipping to determine shared vs. task-specific synergies. Correlation analyses found that among four synergies, two synergies were shared between normal walking and slipping. However, the other two synergies were task-specific. Both limbs were contributing to each of the four synergies, suggesting substantial inter-limb coordination during gait and slip. These findings stay consistent with previous unilateral studies that reported similar synergies between unperturbed and perturbed walking. Activation coefficients corresponding to the two shared synergies were similar between normal walking and slipping for the first 200 ms after heel contact and differed later in stance, suggesting the activation of muscle synergies in response to a slip. A muscle synergy approach would reveal the used sub-tasks during slipping, facilitating identification of impaired sub-tasks, and potentially leading to a purposeful rehabilitation based on damaged sub-functions.