77 research outputs found
Learning an Orchestra Conductor's Technique Using a Wearable Sensor Platform
Our study focuses on finding new input devices for a system allowing users with any skill to configure and conduct a virtual orchestra in real-time. As a first step, we conducted a user study to learn more about the interaction between a conductor's gestures and the orchestra 's reaction. During an orchestra rehearsal session, we observed a conductor's timing and gestures using the eWatch, a wrist-worn wearable computer and sensor platform. The gestures are analyzed and compared to the music of the orchestra
Coaching or gaming? Implications of strategy choice for home based stroke rehabilitation
Background: The enduring aging of the world population and prospective increase of age-related chronic diseases urge the implementation of new models for healthcare delivery. One strategy relies on ICT (Information and Communications Technology) home-based solutions allowing clients to pursue their treatments without institutionalization. Stroke survivors are a particular population that could strongly benefit from such solutions, but is not yet clear what the best approach is for bringing forth an adequate and sustainable usage of home-based rehabilitation systems. Here we explore two possible approaches: coaching and gaming. Methods: We performed trials with 20 healthy participants and 5 chronic stroke survivors to study and compare execution of an elbow flexion and extension task when performed within a coaching mode that provides encouragement or within a gaming mode. For each mode we analyzed compliance, arm movement kinematics and task scores. In addition, we assessed the usability and acceptance of the proposed modes through a customized self-report questionnaire. Results: In the healthy participants sample, 13/20 preferred the gaming mode and rated it as being significantly more fun (p < .05), but the feedback delivered by the coaching mode was subjectively perceived as being more useful (p < .01). In addition, the activity level (number of repetitions and total movement of the end effector) was significantly higher (p <.001) during coaching. However, the quality of movements was superior in gaming with a trend towards shorter movement duration (p=.074), significantly shorter travel distance (p <.001), higher movement efficiency (p <.001) and higher performance scores (p <.001). Stroke survivors also showed a trend towards higher activity levels in coaching, but with more movement quality during gaming. Finally, both training modes showed overall high acceptance. Conclusions: Gaming led to higher enjoyment and increased quality in movement execution in healthy participants. However, we observed that game mechanics strongly determined user behavior and limited activity levels. In contrast, coaching generated higher activity levels. Hence, the purpose of treatment and profile of end-users has to be considered when deciding on the most adequate approach for home based stroke rehabilitation
An exploratory study on techniques for quantitative assessment of stroke rehabilitation exercises
Technology-assisted systems to monitor and assess rehabilitation
exercises have an opportunity of enhancing rehabilitation practices
by automatically collecting patient’s quantitative performance data.
However, even if a complex algorithm (e.g. Neural Network) is
applied, it is still challenging to develop such a system due to pa tients with various physical conditions. The system with a complex
algorithm is limited to be a black-box system that cannot provide
explanations on its predictions. To address these challenges, this
paper presents a hybrid model that integrates a machine learn ing (ML) model with a rule-based (RB) model as an explainable
artificial intelligence (AI) technique for quantitative assessment of
stroke rehabilitation exercises. For evaluation, we collected thera pist’s knowledge on assessment as 15 rules from interviews with
therapists and the dataset of three upper-limb stroke rehabilitation
exercises from 15 post-stroke and 11 healthy subjects using a Kinect
sensor. Experimental results show that a hybrid model can achieve
comparable performance with a ML model using Neural Network,
but also provide explanations on a model prediction with a RB
model. The results indicate the potential of a hybrid model as an
explainable AI technique to support the interpretation of a model
and fine-tune a model with user-specific rules for personalization.info:eu-repo/semantics/publishedVersio
Towards personalized interaction and corrective feedback of a socially assistive robot for post-stroke rehabilitation therapy
A robotic exercise coaching system requires the
capability of automatically assessing a patient’s exercise to in teract with a patient and generate corrective feedback. However,
even if patients have various physical conditions, most prior
work on robotic exercise coaching systems has utilized generic,
pre-defined feedback.
This paper presents an interactive approach that combines
machine learning and rule-based models to automatically assess
a patient’s rehabilitation exercise and tunes with patient’s
data to generate personalized corrective feedback. To generate
feedback when an erroneous motion occurs, our approach
applies an ensemble voting method that leverages predictions
from multiple frames for frame-level assessment. According to
the evaluation with the dataset of three stroke rehabilitation
exercises from 15 post-stroke subjects, our interactive approach
with an ensemble voting method supports more accurate frame level assessment (p < 0.01), but also can be tuned with held-out
user’s unaffected motions to significantly improve the perfor mance of assessment from 0.7447 to 0.8235 average F1-scores
over all exercises (p < 0.01). This paper discusses the value of
an interactive approach with an ensemble voting method for
personalized interaction of a robotic exercise coaching system.info:eu-repo/semantics/publishedVersio
Activity recognition and monitoring using multiple sensors on different body positions.
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