FinDroidHR: smartwatch gesture input with optical heartrate monitor

Yu Zhang; Tao Gu; Chu Luo; Vassilis Kostakos; Aruna Seneviratne

doi:10.1145/3191788

FinDroidHR: smartwatch gesture input with optical heartrate monitor

Yu Zhang, Tao Gu, Chu Luo, Vassilis Kostakos, Aruna Seneviratne

Research output: Contribution to journal › Article › peer-review

Abstract

We present FinDroidHR, a novel gesture input technique for off-the-shelf smartwatches. Our technique is designed to detect 10 hand gestures on the hand wearing a smartwatch. The technique is enabled by analysing features of the Photoplethysmography (PPG) signal that optical heart-rate sensors capture. In a study with 20 participants, we show that FinDroidHR achieves 90.55% accuracy and 90.73% recall. Our work is the first study to explore the feasibility of using optical sensors on the off-the-shelf wearable devices to recognise gestures. Without requiring bespoke hardware, FinDroidHR can be readily used on existing smartwatches.

Original language	English
Article number	56
Number of pages	42
Journal	ACM Proceedings on Interactive, Mobile, Wearable and Ubiquitous Technologies
Volume	2
Issue number	1
DOIs	https://doi.org/10.1145/3191788
Publication status	Published - Mar 2018
Externally published	Yes

Keywords

Wearable Computing
Gesture Interaction
Machine Learning
Mobile Sensors

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10.1145/3191788

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abstract = "We present FinDroidHR, a novel gesture input technique for off-the-shelf smartwatches. Our technique is designed to detect 10 hand gestures on the hand wearing a smartwatch. The technique is enabled by analysing features of the Photoplethysmography (PPG) signal that optical heart-rate sensors capture. In a study with 20 participants, we show that FinDroidHR achieves 90.55% accuracy and 90.73% recall. Our work is the first study to explore the feasibility of using optical sensors on the off-the-shelf wearable devices to recognise gestures. Without requiring bespoke hardware, FinDroidHR can be readily used on existing smartwatches.",

keywords = "Wearable Computing, Gesture Interaction, Machine Learning, Mobile Sensors",

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AU - Zhang, Yu

AU - Gu, Tao

AU - Luo, Chu

AU - Kostakos, Vassilis

AU - Seneviratne, Aruna

PY - 2018/3

Y1 - 2018/3

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AB - We present FinDroidHR, a novel gesture input technique for off-the-shelf smartwatches. Our technique is designed to detect 10 hand gestures on the hand wearing a smartwatch. The technique is enabled by analysing features of the Photoplethysmography (PPG) signal that optical heart-rate sensors capture. In a study with 20 participants, we show that FinDroidHR achieves 90.55% accuracy and 90.73% recall. Our work is the first study to explore the feasibility of using optical sensors on the off-the-shelf wearable devices to recognise gestures. Without requiring bespoke hardware, FinDroidHR can be readily used on existing smartwatches.

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KW - Gesture Interaction

KW - Machine Learning

KW - Mobile Sensors

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FinDroidHR: smartwatch gesture input with optical heartrate monitor

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