User Independent Gesture Interaction for Small Handheld Devices

2009 International Conference on Computational Science and Engineering

French

et al. 2009

Tröster, G. (2009). Performance analysis of an HMM-based gesture recognition using a wristwatch device. In Proceedings of the 7th IEEE International Conference on Computational Science and Engineering, CSE'09, 29-31 August 2009, Vancouver, British Columbia (pp. 303-309). Piscataway: Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/CSE.2009 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. This work presents a watch device with an integrated gesture recognition interface. We report the resource-optimized implementation of our algorithmic solution on the watch and demonstrate that the recognition approach is feasible for such constraint devoices. The system is wearable during everyday activities and was evaluated with eight users to complete questionnaires through intuitive one-hand movements.We developed a procedure to spot and classify input gestures from continuous acceleration data acquired by the watch. The recognition procedure is based on hidden Markov models (HMM) and was fully implemented on a watch. The algorithm achieved an average recall of 79% at 93% precision in recognizing the relevant gestures. The watch implementation of continuous gesture spotting showed a delay below 3 ms for feature computation, Viterbi path processing, and final classification at less than 4 KB memory usage.

Section: B Processing Performance Resultsmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Performance Analysis of an HMM-Based Gesture Recognition Using a Wristwatch Device

Amstutz

2009 International Conference on Computational Science and Engineering

French

et al. 2009

“…Examples of motion and gesture recognition include immersive gaming [8], [9], and many forms of computer interaction, e.g. [10], [11], [12]. Sign language recognition is a closely related topic and considered in several works, e.g.…”

Section: B Related Workmentioning

confidence: 99%

Adaptive Activity Spotting Based on Event Rates

2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing

2010

Abstract-To date many activity spotting approaches are static: once the system is trained and deployed it does not change anymore. There are substantial shortcomings of this approach, specifically spotting performance is hampered when patterns or sensor noise level changes.In this work an unsupervised sensitivity adaptation mechanism is proposed for activity event spotting based on expected activity event rates. The expected event rate for activity spotting was derived from the generalisation metric used in information retrieval. To illustrate generalisation effects and depict relations of spotting performance and event rate, different event rates were simulated and their precision-recall spotting performance analysed. Subsequently, the sensitivity adaptation concept is presented and evaluated. For this purpose two large datasets from personal healthcare applications were considered to explore benefits and limitations of this adaptation approach: recognition of drinking motions from inertial sensors and chewing strokes from sound. Results showed up to 28% spotting performance increase for event rate adapted operation, confirming performance benefits for sensitivity adaptation. The approach will be most applicable in situations, where estimated event rate statistics show low variance and long monitoring durations allow effective sensitivity adaptations.

“…Kallio et al [9] presented an application using acceleration sensors embedded in a remote control to manage home appliances. Their work was focused on confirming the feasibility for classifying different gestures using hidden Markov models (HMMs).…”

Section: Gesture-operated Mobile Devicesmentioning

confidence: 99%

Gesture-Controlled User Input to Complete Questionnaires on Wrist-Worn Watches

Human-Computer Interaction. Novel Interaction Methods and Techniques

Amstutz

Smailagic

et al. 2009

Abstract. The aim of this work was to investigate arm gestures as an alternative input modality for wrist-worn watches. In particular we implemented a gesture recognition system and questionnaire interface into a watch prototype. We analyzed the wearer's effort and learning performance to use the gesture interface and compared their performance to a classical button-based solution. Moreover we evaluated the system performance to spot wearer gestures and the system responsiveness. Our wearer study showed that the watch achieved a recognition accuracy of more than 90%. Completion times showed a clear decrease from 3 min in the first repetition to 1 min, 49 sec in the last one. Similarly, variance of completion times between wearers decreased during repetitions. Completion time using the button interface was 36 sec. Ratings of physical and concentration effort decreased during the study. Our results confirm that wearer training state is rather reflected in completion time than recognition performance.