Paper
WatchHand: Enabling Continuous Hand Pose Tracking On Off-the-Shelf Smartwatches
Authors
Jiwan Kim, Chi-Jung Lee, Hohurn Jung, Tianhong Catherine Yu, Ruidong Zhang, Ian Oakley, Cheng Zhang
Abstract
Tracking hand poses on wrist-wearables enables rich, expressive interactions, yet remains unavailable on commercial smartwatches, as prior implementations rely on external sensors or custom hardware, limiting their real-world applicability. To address this, we present WatchHand, the first continuous 3D hand pose tracking system implemented on off-the-shelf smartwatches using only their built-in speaker and microphone. WatchHand emits inaudible frequency-modulated continuous waves and captures their reflections from the hand. These acoustic signals are processed by a deep-learning model that estimates 3D hand poses for 20 finger joints. We evaluate WatchHand across diverse real-world conditions -- multiple smartwatch models, wearing-hands, body postures, noise conditions, pose-variation protocols -- and achieve a mean per-joint position error of 7.87 mm in cross-session tests with device remounting. Although performance drops for unseen users or gestures, the model adapts effectively with lightweight fine-tuning on small amounts of data. Overall, WatchHand lowers the barrier to smartwatch-based hand tracking by eliminating additional hardware while enabling robust, always-available interactions on millions of existing devices.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.21610v1</id>\n <title>WatchHand: Enabling Continuous Hand Pose Tracking On Off-the-Shelf Smartwatches</title>\n <updated>2026-02-25T06:12:43Z</updated>\n <link href='https://arxiv.org/abs/2602.21610v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.21610v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Tracking hand poses on wrist-wearables enables rich, expressive interactions, yet remains unavailable on commercial smartwatches, as prior implementations rely on external sensors or custom hardware, limiting their real-world applicability. To address this, we present WatchHand, the first continuous 3D hand pose tracking system implemented on off-the-shelf smartwatches using only their built-in speaker and microphone. WatchHand emits inaudible frequency-modulated continuous waves and captures their reflections from the hand. These acoustic signals are processed by a deep-learning model that estimates 3D hand poses for 20 finger joints. We evaluate WatchHand across diverse real-world conditions -- multiple smartwatch models, wearing-hands, body postures, noise conditions, pose-variation protocols -- and achieve a mean per-joint position error of 7.87 mm in cross-session tests with device remounting. Although performance drops for unseen users or gestures, the model adapts effectively with lightweight fine-tuning on small amounts of data. Overall, WatchHand lowers the barrier to smartwatch-based hand tracking by eliminating additional hardware while enabling robust, always-available interactions on millions of existing devices.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.HC'/>\n <published>2026-02-25T06:12:43Z</published>\n <arxiv:comment>This work will be presented and published at ACM CHI 2026</arxiv:comment>\n <arxiv:primary_category term='cs.HC'/>\n <author>\n <name>Jiwan Kim</name>\n </author>\n <author>\n <name>Chi-Jung Lee</name>\n </author>\n <author>\n <name>Hohurn Jung</name>\n </author>\n <author>\n <name>Tianhong Catherine Yu</name>\n </author>\n <author>\n <name>Ruidong Zhang</name>\n </author>\n <author>\n <name>Ian Oakley</name>\n </author>\n <author>\n <name>Cheng Zhang</name>\n </author>\n </entry>"
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