Contrasting paradigms for the development of wearable computers
IBM Systems Journal
Analyzing features for activity recognition
Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control)
Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control)
Proceedings of the 1st ACM SIGMOBILE international workshop on Systems and networking support for healthcare and assisted living environments
Proceedings of the 5th international conference on Embedded networked sensor systems
A review of smart homes-Present state and future challenges
Computer Methods and Programs in Biomedicine
Activity recognition from interactions with objects using dynamic Bayesian network
Proceedings of the 3rd ACM International Workshop on Context-Awareness for Self-Managing Systems
Proceedings of the 3rd ACM International Workshop on Context-Awareness for Self-Managing Systems
Analysis of Time and Frequency Domain Features of Accelerometer Measurements
ICCCN '09 Proceedings of the 2009 Proceedings of 18th International Conference on Computer Communications and Networks
Mercury: a wearable sensor network platform for high-fidelity motion analysis
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
On the choice and placement of wearable vision sensors
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Wearable EOG goggles: Seamless sensing and context-awareness in everyday environments
Journal of Ambient Intelligence and Smart Environments
Movement-based group awareness with wireless sensor networks
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
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This paper investigates the impact of placement and orientation variations on the quality of sensed data. Different types of human movements are considered, namely, balancing, skipping, leaping; climbing up and down a staircase, and running. For data collection, tri-axis accelerometer sensors are used. As target placements, arms, thighs, knees, ankle, and waist are considered. Likewise, four different orientation angles were considered during deployment, namely, 0, 30, 45, and 85 degrees. The features employed to investigate placement and orientation variations were zero\mean-value crossing rate, correlation coefficients, cross-correlation, and auto-correlation. A particular focus was given to steady slow movements (climbing up and down a staircase) and steady fast movements (running). Remarkably, the fast movements are less affected by placement variations in comparison to the slow movements. Moreover, it will be shown that the effect of orientation variations for all types of movements are insignificant when absolute acceleration instead of the accelerations of individual axes are independently considered.