ACM SIGCHI curricula for human-computer interaction
ACM SIGCHI curricula for human-computer interaction
HMM Based On-Line Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An HMM-Based Threshold Model Approach for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Senior CHI: how can we make technology "elder-friendly?"
CHI '99 Extended Abstracts on Human Factors in Computing Systems
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
Discovering multivariate motifs using subsequence density estimation and greedy mixture learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Philips stroke rehabilitation exerciser: a usability test
Telehealth/AT '08 Proceedings of the IASTED International Conference on Telehealth/Assistive Technologies
Activity recognition using biomechanical model based pose estimation
EuroSSC'10 Proceedings of the 5th European conference on Smart sensing and context
An Integrated Mobile System for Long-Term Aerobic Activity Monitoring and Support in Daily Life
TRUSTCOM '12 Proceedings of the 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications
Home-based health monitoring of the elderly through gait recognition
Journal of Ambient Intelligence and Smart Environments - Home-based Health and Wellness Measurement and Monitoring
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Regular and moderate physical activity practice provides many physiological benefits. It reduces the risk of disease outcomes and is the basis for proper rehabilitation after a severe disease. Aerobic activity and strength exercises are strongly recommended in order to maintain autonomy with ageing. Balanced activity of both types is important, especially to the elderly population. Several methods have been proposed to monitor aerobic activities. However, no appropriate method is available for controlling more complex parameters of strength exercises. Within this context, the present article introduces a personalized, home-based strength exercise trainer designed for the elderly. The system guides a user at home through a personalized exercise program. Using a network of wearable sensors the user's motions are captured. These are evaluated by comparing them to prescribed exercises, taking both exercise load and technique into account. Moreover, the evaluation results are immediately translated into appropriate feedback to the user in order to assist the correct exercise execution. Besides the direct feedback, a major novelty of the system is its generic personalization by means of a supervised teach-in phase, where the program is performed once under supervision of a physical activity specialist. This teach-in phase allows the system to record and learn the correct execution of exercises for the individual user and to provide personalized monitoring. The user-driven design process, the system development and its underlying activity monitoring methodology are described. Moreover, technical evaluation results as well as results concerning the usability of the system for ageing people are presented. The latter has been assessed in a clinical study with thirty participants of 60 years or older, some of them showing usual diseases or functional limitations observed in elderly population.