This paper presents the design of a home-based adaptive mixed reality system (HAMRR) for upper extremity stroke rehabilitation. The goal of HAMRR is to help restore motor function to chronic stroke survivors by providing an engaging long-term reaching task therapy at home. The system uses an intelligent adaptation scheme to create a continuously challenging and unique multi-year therapy experience. The therapy is overseen by a physical therapist, but day-to-day use of the system can be independently set up and completed by a stroke survivor. The HAMMR system tracks movement of the wrist and torso and provides real-time, post-trial, and post-set feedback to encourage the stroke survivor to self-assess his or her movement and engage in active learning of new movement strategies. The HAMRR system consists of a custom table, chair, and media center, and is designed to easily integrate into any home.
<p><b>This thesis develops new methods to acquire information about observable aspects of flute playing. Performance parameters, such as the changes in expelled breath, the forearm muscle movements, the variation in the applied pressure at the flute fulcrum points, and the modification of the flute’s spatial orientation, can reveal the physical actions exhibited by the musician during playing. Examining quantifiable characteristics such as these from a technological perspective could provide researchers with a more complete understanding of flute performance techniques. However, existing data capture and analysis methods lend themselves to inaccuracy and tend to occur primarily offline.</b></p> <p>Gesture and audio signals from experienced flutists were recorded while they performed lyrical études. This provided a corpus of hybrid signal data that revealed performance techniques. To acquire this data, a hybrid signal sensing system, a new flute-based audio onset detection algorithm, and a new hybrid signal onset detection algorithm specific to flute audio and gesture features were created. These methods, which operate online with low latency, result in the ability to more accurately assess note onset and to track various physical motions and movements of a flute player.</p> <p>Results are presented from the analyses of the captured audio, the gesture features, and a fusion thereof. Including gesture feature sensing with the audio data improves upon an audio-only approach for flute note onset detection. Analyzing gesture features gives insight into movements that occur during playing. Additionally, a proposed visual feedback application shows how we can graphically present this corpus of captured data.</p>
Thrii is a multimodal interactive installation that explores levels of movement similarity among its participants. Each of the three participants manipulates a large spherical object whose movement is tracked via an embedded accelerometer. An analysis engine computes the similarity of movement for each possible pair of objects, as well as self-similarity (e.g., repetition of movement over time) for each object. The extent of similarity among the movements of each object is communicated by a visualization projected on a three-sided pyramid, a non-directional audio environment, and lighting produced by the spherical objects. The installation's focus is intended to examine notions of collaboration between participants. We have found that participants engage with Thrii through exploration of collaborative gestures.
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