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.

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.

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.