Wearable vibrotactile stimulation for upper extremity rehabilitation in chronic stroke: clinical feasibility trial using the VTS Glove.

OBJECTIVE: Evaluate the feasibility and potential impacts on hand function using a wearable stimulation device (the VTS Glove) which provides mechanical, vibratory input to the affected limb of chronic stroke survivors. METHODS: A double-blind, randomized, controlled feasibility study including sixteen chronic stroke survivors (mean age: 54; 1-13 years post-stroke) with diminished movement and tactile perception in their affected hand. Participants were given a wearable device to take home and asked to wear it for three hours daily over eight weeks. The device intervention was either (1) the VTS Glove, which provided vibrotactile stimulation to the hand, or (2) an identical glove with vibration disabled. Participants were randomly assigned to each condition. Hand and arm function were measured weekly at home and in local physical therapy clinics. RESULTS: Participants using the VTS Glove showed significantly improved Semmes-Weinstein monofilament exam results, reduction in Modified Ashworth measures in the fingers, and some increased voluntary finger flexion, elbow and shoulder range of motion. CONCLUSIONS: Vibrotactile stimulation applied to the disabled limb may impact tactile perception, tone and spasticity, and voluntary range of motion. Wearable devices allow extended application and study of stimulation methods outside of a clinical setting.

Quantifying canine interactions with smart toys assesses suitability for service dog work.

There are approximately a half million active service dogs in the United States, providing life-changing assistance and independence to people with a wide range of disabilities. The tremendous value of service dogs creates significant demand, which service dog providers struggle to meet. Breeding, raising, and training service dogs is an expensive, time-consuming endeavor which is exacerbated by expending resources on dogs who ultimately will prove to be unsuitable for service dog work because of temperament issues. Quantifying behavior and temperament through sensor-instrumented dog toys can provide a way to predict which dogs will be suitable for service dog work, allowing resources to be focused on the dogs likely to succeed. In a 2-year study, we tested dogs in advanced training at Canine Companions for Independence with instrumented toys, and we discovered that a measure of average bite duration is significantly correlated with a dog's placement success as a service dog [Adjusted OR = 0.12, Pr(>|z|) = 0.00666]. Applying instrumented toy interactions to current behavioral assessments could yield more accurate measures for predicting successful placement of service dogs while reducing the workload of the trainers.

Design of a Wearable Vibrotactile Stimulation Device for Individuals With Upper-Limb Hemiparesis and Spasticity.

Vibratory stimulation may improve post-stroke symptoms such as spasticity; however, current studies are limited by the large, clinic-based apparatus used to apply this stimulation. A wearable device could provide vibratory stimulation in a mobile form, enabling further study of this technique. An initial device, the vibrotactile stimulation (VTS) Glove, was deployed in an eight-week clinical study in which sixteen individuals with stroke used the device for several hours daily. Participants reported wearing the glove during activities such as church, social events, and dining out. However, 69% of participants struggled to extend or insert their fingers to don the device. In a follow-up study, eight individuals with stroke evaluated new VTS device prototypes in a three-round iterative design study with the aims of creating the next generation of VTS devices and understanding features that influence interaction with a wearable device by individuals with impaired upper-limb function. Interviews and interaction tasks were used to define actionable design revisions between each round of evaluation. Our analysis identified six new themes from participants regarding device designs: hand supination is challenging, separate finger attachments inhibit fit and use, fingers may be flexed or open, fabric coverage impacts comfort, a reduced concern for social comfort, and the affected hand is infrequently used. Straps that wrap around the arm and fixtures on the anterior arm were other challenging features. We discuss potential accommodations for these challenges, as well as social comfort. New VTS device designs are presented and were donned in an average time of 48 seconds.

EarBit: Using Wearable Sensors to Detect Eating Episodes in Unconstrained Environments.

Chronic and widespread diseases such as obesity, diabetes, and hypercholesterolemia require patients to monitor their food intake, and food journaling is currently the most common method for doing so. However, food journaling is subject to self-bias and recall errors, and is poorly adhered to by patients. In this paper, we propose an alternative by introducing EarBit, a wearable system that detects eating moments. We evaluate the performance of inertial, optical, and acoustic sensing modalities and focus on inertial sensing, by virtue of its recognition and usability performance. Using data collected in a simulated home setting with minimum restrictions on participants' behavior, we build our models and evaluate them with an unconstrained outside-the-lab study. For both studies, we obtained video footage as ground truth for participants activities. Using leave-one-user-out validation, EarBit recognized all the eating episodes in the semi-controlled lab study, and achieved an accuracy of 90.1% and an F1-score of 90.9% in detecting chewing instances. In the unconstrained, outside-the-lab evaluation, EarBit obtained an accuracy of 93% and an F1-score of 80.1% in detecting chewing instances. It also accurately recognized all but one recorded eating episodes. These episodes ranged from a 2 minute snack to a 30 minute meal.

Activity recognition of assembly tasks using body-worn microphones and accelerometers.

In order to provide relevant information to mobile users, such as workers engaging in the manual tasks of maintenance and assembly, a wearable computer requires information about the user's specific activities. This work focuses on the recognition of activities that are characterized by a hand motion and an accompanying sound. Suitable activities can be found in assembly and maintenance work. Here, we provide an initial exploration into the problem domain of continuous activity recognition using on-body sensing. We use a mock "wood workshop" assembly task to ground our investigation. We describe a method for the continuous recognition of activities (sawing, hammering, filing, drilling, grinding, sanding, opening a drawer, tightening a vise, and turning a screwdriver) using microphones and three-axis accelerometers mounted at two positions on the user's arms. Potentially "interesting" activities are segmented from continuous streams of data using an analysis of the sound intensity detected at the two different locations. Activity classification is then performed on these detected segments using linear discriminant analysis (LDA) on the sound channel and hidden Markov models (HMMs) on the acceleration data. Four different methods at classifier fusion are compared for improving these classifications. Using user-dependent training, we obtain continuous average recall and precision rates (for positive activities) of 78 percent and 74 percent, respectively. Using user-independent training (leave-one-out across five users), we obtain recall rates of 66 percent and precision rates of 63 percent. In isolation, these activities were recognized with accuracies of 98 percent, 87 percent, and 95 percent for the user-dependent, user-independent, and user-adapted cases, respectively.