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Guannan Huang

dblp:134/2813 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none

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

Human-computer interaction and ubiquitous 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
Health and well-being technologies · 62% Wearable and physiological sensing · 19% Haptics and multimodal interaction · 19%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Health and well-being technologies › rehabilitation
rehabilitation training
0.212013
COPDTrainer: a smartphone-based motion rehabilitation training system with real-time acoustic feedback · UbiComp 2013
Haptics and multimodal interaction
auditory feedback
0.012013
COPDTrainer: a smartphone-based motion rehabilitation training system with real-time acoustic feedback · UbiComp 2013
Wearable and physiological sensing › inertial sensing
smartphone inertial sensing
0.012013
COPDTrainer: a smartphone-based motion rehabilitation training system with real-time acoustic feedback · UbiComp 2013

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

sinusoidal motion model · 0.2inertial sensing · 0.2
YearPublicationVenuePosition
2013 COPDTrainer: a smartphone-based motion rehabilitation training system with real-time acoustic feedback
abstract
Patient motion training requires adaptive, personalized exercise models and systems that are easy to handle. In this paper, we evaluate a training system based on a smartphone that integrates in clinical routines and serves as a tool for therapist and patient. Only the smartphone's build-in inertial sensors were used to monitor exercise execution and providing acoustic feedback on exercise performance and exercise errors. We used a sinusoidal motion model to exploit the typical repetitive structure of motion exercises. A Teach-mode was used to personalize the system by training under the guidance of a therapist and deriving exercise model parameters. Subsequently, in a Train-mode, the system provides exercise feedback. We validate our approach in a validation with healthy volunteers and in an intervention study with COPD patients. System performance, trainee performance, and feedback efficacy were analysed. We further compare the therapist and training system performances and demonstrate that our approach is viable.
Gabriele Spina, Guannan Huang, Anouk Vaes, Martijn Spruit, Oliver Amft
UbiComp2