BackgroundSensory augmentation has been shown to improve postural stability during real-time balance applications. Limited long-term controlled studies have examined retention of balance improvements in healthy older adults after training with sensory augmentation has ceased. This pilot study aimed to assess the efficacy of long-term balance training with and without sensory augmentation among community-dwelling healthy older adults.MethodsTwelve participants (four males, eight females; 75.6 ± 4.9 yrs) were randomly assigned to the experimental group (n = 6) or control group (n = 6). Participants trained in their homes for eight weeks, completing three 45-min exercise sessions per week using smart phone balance trainers that provided written, graphic, and video guidance, and monitored trunk sway. During each session, participants performed six repetitions of six exercises selected from five categories (static standing, compliant surface standing, weight shifting, modified center of gravity, and gait). The experimental group received vibrotactile sensory augmentation for four of the six repetitions per exercise via the smart phone balance trainers, while the control group performed exercises without sensory augmentation. The smart phone balance trainers sent exercise performance data to a physical therapist, who recommended exercises on a weekly basis. Balance performance was assessed using a battery of clinical balance tests (Activity Balance Confidence Scale, Sensory Organization Test, Mini Balance Evaluation Systems Test, Five Times Sit to Stand Test, Four Square Step Test, Functional Reach Test, Gait Speed Test, Timed Up and Go, and Timed Up and Go with Cognitive Task) before training, after four weeks of training, and after eight weeks of training.ResultsParticipants in the experimental group were able to use vibrotactile sensory augmentation independently in their homes. After training, the experimental group had significantly greater improvements in Sensory Organization Test and Mini Balance Evaluation Systems Test scores than the control group. Significant improvement was also observed for Five Times Sit to Stand Test duration within the experimental group, but not in the control group. No significant improvements between the two groups were observed in the remaining clinical outcome measures.ConclusionThe findings of this study support the use of sensory augmentation devices by community-dwelling healthy older adults as balance rehabilitation tools, and indicate feasibility of telerehabilitation therapy with reduced input from clinicians.

BACKGROUND AND OBJECTIVE: This pilot study aimed to investigate the effects of incorporating vibrotactile sensory augmentation (SA) on balance performance among people with unilateral vestibular disorders (UVD). METHODS: Eight participants with UVD were recruited. Participants completed 18 balance training sessions across six weeks in a clinical setting. Four participants (68.1 ± 7.5 yrs) were randomized to the experimental group (EG) and received trunk-based vibrotactile SA while performing the balance exercises, and four participants (63.1 ± 11.3 yrs) were assigned to the control group (CG); CG participants completed the balance training without SA. Clinical and kinematic balance performance measures were collected before training; midway through training; and one week, one month, and six months after training. RESULTS: All participants, regardless of group, demonstrated improvements in a subset of the clinical or balance metrics immediately following completion of the balance training protocol. The EG showed significantly greater improvements than the CG for the Activities-specific Balance Confidence Scale and postural stability during the two standing balance exercises with head movements. The EG also had larger improvements than the CG for the Sensory Organization Test (SOT), Mini Balance Evaluations Systems Test, Gait Speed Test, Dynamic Gait Index, Functional Gait Assessment, and vestibular reliance metric calculated based on the SOT. CONCLUSIONS: Incorporating vibrotactile SA into vestibular rehabilitation programs may lead to additional benefits that may be retained up to six months after training compared to training without vibrotactile SA. A larger study is warranted to demonstrate statistical significance between the groups.

Using a "split -mix" synthesis approach, "One-Bead One-Compound" (OBOC) combinatorial libraries can be generated such that each bead displays only one chemical entity.Tens of thousands to millions of compound-beads can be screened concurrently using a variety of biochemical and cell-based screening methods. Positive beads are then physically isolated for structure determination. Peptide beads or peptoid beads consisting of a-amino acids and with a free N-terminus can be routinely sequenced by an automatic microsequencer using Edman chemistry. Libraries with N-terminally blocked peptides, peptides with unsequenceable building blocks, or small molecules require encoding. To fully exploit the OBOC combinatorial library methods, we have developed topologically segregated bilayer beads. Such bilayer beads allow us to prepare library compound on the outer layer of each bead and the coding tags in the bead interior. In addition, we can use these bilayer beads to prepare OBOC combinatorial libraries that are down-substituted on the bead surface but fully substituted in the bead interior. This configuration enables one to screen at a much higher stringency and yet have enough peptides or coding tags retained in the bead interior for structure determination.

BackgroundPostural balance and gait training is important for treating persons with functional impairments, however current systems are generally not portable and are unable to train different types of movements.MethodsThis paper describes a proof-of-concept design of a configurable, wearable sensing and feedback system for real-time postural balance and gait training targeted for home-based treatments and other portable usage. Sensing and vibrotactile feedback are performed via eight distributed, wireless nodes or “Dots” (size: 22.5 × 20.5 × 15.0 mm, weight: 12.0 g) that can each be configured for sensing and/or feedback according to movement training requirements. In the first experiment, four healthy older adults were trained to reduce medial-lateral (M/L) trunk tilt while performing balance exercises. When trunk tilt deviated too far from vertical (estimated via a sensing Dot on the lower spine), vibrotactile feedback (via feedback Dots placed on the left and right sides of the lower torso) cued participants to move away from the vibration and back toward the vertical no feedback zone to correct their posture. A second experiment was conducted with the same wearable system to train six healthy older adults to alter their foot progression angle in real-time by internally or externally rotating their feet while walking. Foot progression angle was estimated via a sensing Dot adhered to the dorsal side of the foot, and vibrotactile feedback was provided via feedback Dots placed on the medial and lateral sides of the mid-shank cued participants to internally or externally rotate their foot away from vibration.ResultsIn the first experiment, the wearable system enabled participants to significantly reduce trunk tilt and increase the amount of time inside the no feedback zone. In the second experiment, all participants were able to adopt new gait patterns of internal and external foot rotation within two minutes of real-time training with the wearable system.ConclusionThese results suggest that the configurable, wearable sensing and feedback system is portable and effective for different types of real-time human movement training and thus may be suitable for home-based or clinic-based rehabilitation applications.

Compared to in-clinic balance training, in-home training is not as effective. This is, in part, due to the lack of feedback from physical therapists (PTs). Here, we analyze the feasibility of using trunk sway data and machine learning (ML) techniques to automatically evaluate balance, providing accurate assessments outside of the clinic. We recruited sixteen participants to perform standing balance exercises. For each exercise, we recorded trunk sway data and had a PT rate balance performance on a scale of 1 to 5. The rating scale was adapted from the Functional Independence Measure. From the trunk sway data, we extracted a 61-dimensional feature vector representing performance of each exercise. Given these labeled data, we trained a multi-class support vector machine (SVM) to map trunk sway features to PT ratings. Evaluated in a leave-one-participant-out scheme, the model achieved a classification accuracy of 82%. Compared to participant self-assessment ratings, the SVM outputs were significantly closer to PT ratings. The results of this pilot study suggest that in the absence of PTs, ML techniques can provide accurate assessments during standing balance exercises. Such automated assessments could reduce PT consultation time and increase user compliance outside of the clinic.