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Juhani Ruutiainen

dblp:75/8071 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Haptics and multimodal interaction · 70% Accessibility and assistive technology · 30%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Accessibility and assistive technology
assistive technology
0.112009
Using Haptic Feedback to Improve Grasp Force Control in Multiple Sclerosis Patients · IEEE Trans. Robotics 2009
Haptics and multimodal interaction › haptic device control
grasp force control
0.112009
Using Haptic Feedback to Improve Grasp Force Control in Multiple Sclerosis Patients · IEEE Trans. Robotics 2009
Haptics and multimodal interaction
haptic feedback
0.112009
Using Haptic Feedback to Improve Grasp Force Control in Multiple Sclerosis Patients · IEEE Trans. Robotics 2009
Haptics and multimodal interaction › haptic feedback
vibrotactile feedback
0.012009
Using Haptic Feedback to Improve Grasp Force Control in Multiple Sclerosis Patients · IEEE Trans. Robotics 2009

Methods — techniques the papers use, named apart from their topics

user study · 0.1proportional vibration feedback · 0.1force sensing · 0.1
YearPublicationVenuePosition
2009 Using Haptic Feedback to Improve Grasp Force Control in Multiple Sclerosis Patients
abstract
We describe a simple and low-cost system that can help multiple sclerosis (MS) patients with asymmetric impairment to exert better grasp force control in manipulation tasks. The approach consists of measuring force vectors at the fingertips of the impaired hand, computing the force imbalance among the fingers, and providing corresponding haptic signals to the fingers of the opposite hand. Tests conducted on 24 MS patients indicated that for those with mild impairment, slightly better results were obtained with an ldquoevent-cuerdquo feedback (ECF) that alerted them when the grasp forces were straying outside of a desirable range. For patients with more severe impairment, better results were obtained by providing a proportional signal, in which the frequency and duty cycle of vibration pulses were correlated directly with the magnitudes of the fingertip forces. Post-test surveys of the patients also indicated that mildly impaired subjects preferred an event-cue feedback, and more severely impaired subjects preferred the proportional feedback.
Mark R. Cutkosky, Juhani Ruutiainen, Roope Raisamo
IEEE Trans. Robotics3