jNode: A sensor network platform that supports distributed inertial kinematic monitoring

Scholl, Philipp M.; Laerhoven, Kristof Van; Gordon, Dawud; Scholz, Markus; Berning, Matthias

doi:10.1109/inss.2012.6240553

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…In [12], authors proposed a sensor node platform, in which authors introduced two microcontroller based architecture for kinematic sensor with a µSD card for longterm storage of the data. The core microcontroller is Jennic JN5148 which supports IEEE802.15.4 radio transceiver.…”

Section: Wireless Bio-sensor Platformsmentioning

confidence: 99%

A Study of Wearable Bio-Sensor Technologies and Applications in Healthcare

Mehmood¹,

Nadeem²,

Rizwan³

et al. 2017

SJCMS

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Abstract:In today's world the rapid advancements in Micro-Electromechanical Systems (MEMS) and Nano technology have improved almost all the aspects of daily life routine with the help of different smart devices such as smart phones, compact electronic devices etc. The prime example of these emerging developments is the development of wireless sensors for healthcare procedures. One kind of these sensors is wearable bio-sensors. In this paper, the technologies of two types of bio-sensors (ECG, EMG) are investigated and also compared with traditional ECG, EMG equipment. We have taken SHIMMERTM wireless sensor platform as an example of wearable biosensors technology. We have investigated the systems developed for

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Section: Wireless Bio-sensor Platformsmentioning

confidence: 99%

A Study of Wearable Bio-Sensor Technologies and Applications in Healthcare

Mehmood¹,

Nadeem²,

Rizwan³

et al. 2017

SJCMS

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“…Arm motion was sensed with a sensor equipped wristband. We used a jNode sensor [14], an open research sensor platform which contains a 9 degrees-of-freedom inertial measurement unit (IMU). Sensor data was sampled at 50 Hz and sent wirelessly to a computer.…”

Section: Gesture Recognitionmentioning

confidence: 99%

Design and Evaluation of a Self-Correcting Gesture Interface based on Error Potentials from EEG

Putze

Amma

Schultz

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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Any user interface which automatically interprets the user's input using natural modalities like gestures makes mistakes. System behavior depending on such mistakes will confuse the user and lead to an erroneous interaction flow. The automatic detection of error potentials in electroencephalographic data recorded from a user allows the system to detect such states of confusion and automatically bring the interaction back on track. In this work, we describe the design of such a selfcorrecting gesture interface, implement different strategies to deal with detected errors, use a simulation approach to analyze performance and costs of those strategies and execute a user study to evaluate user satisfaction. We show that selfcorrection significantly improves gesture recognition accuracy at lower costs and with higher acceptance than manual correction.

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