This paper presents ssLOTR (self-supervised learning on the rings), a system that shows the feasibility of designing self-supervised learning based techniques for 3D finger motion tracking using a custom-designed wearable inertial measurement unit (IMU) sensor with a minimal overhead of labeled training data. Ubiquitous finger motion tracking enables a number of applications in augmented and virtual reality, sign language recognition, rehabilitation healthcare, sports analytics, etc. However, unlike vision, there are no large-scale training datasets for developing robust machine learning (ML) models on wearable devices. ssLOTR designs ML models based on data augmentation and self-supervised learning to first extract efficient representations from raw IMU data without the need for any training labels. The extracted representations are further trained with small-scale labeled training data. In comparison to fully supervised learning, we show that only 15% of labeled training data is sufficient with self-supervised learning to achieve similar accuracy. Our sensor device is designed using a two-layer printed circuit board (PCB) to minimize the footprint and uses a combination of Polylactic acid (PLA) and Thermoplastic polyurethane (TPU) as housing materials for sturdiness and flexibility. It incorporates a system-on-chip (SoC) microcontroller with integrated WiFi/Bluetooth Low Energy (BLE) modules for real-time wireless communication, portability, and ubiquity. In contrast to gloves, our device is worn like rings on fingers, and therefore, does not impede dexterous finger motion. Extensive evaluation with 12 users depicts a 3D joint angle tracking accuracy of 9.07° (joint position accuracy of 6.55mm) with robustness to natural variation in sensor positions, wrist motion, etc, with low overhead in latency and power consumption on embedded platforms.
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Leveraging the Properties of mmWave Signals for 3D Finger Motion Tracking for Interactive IoT Applications
mmWave signals form a critical component of 5G and next-generation wireless networks, which are also being increasingly considered for sensing the environment around us to enable ubiquitous IoT applications. In this context, this paper leverages the properties of mmWave signals for tracking 3D finger motion for interactive IoT applications. While conventional vision-based solutions break down under poor lighting, occlusions, and also suffer from privacy concerns, mmWave signals work under typical occlusions and non-line-of-sight conditions, while being privacy-preserving. In contrast to prior works on mmWave sensing that focus on predefined gesture classification, this work performs continuous 3D finger motion tracking. Towards this end, we first observe via simulations and experiments that the small size of fingers coupled with specular reflections do not yield stable mmWave reflections. However, we make an interesting observation that focusing on the forearm instead of the fingers can provide stable reflections for 3D finger motion tracking. Muscles that activate the fingers extend through the forearm, whose motion manifests as vibrations on the forearm. By analyzing the variation in phases of reflected mmWave signals from the forearm, this paper designs mm4Arm, a system that tracks 3D finger motion. Nontrivial challenges arise due to the high dimensional search space, complex vibration patterns, diversity across users, hardware noise, etc. mm4Arm exploits anatomical constraints in finger motions and fuses them with machine learning architectures based on encoder-decoder and ResNets in enabling accurate tracking. A systematic performance evaluation with 10 users demonstrates a median error of 5.73° (location error of 4.07 mm) with robustness to multipath and natural variation in hand position/orientation. The accuracy is also consistent under non-line-of-sight conditions and clothing that might occlude the forearm. mm4Arm runs on smartphones with a latency of 19 ms and low energy overhead.
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- PAR ID:
- 10395298
- Date Published:
- Journal Name:
- Proceedings of the ACM on Measurement and Analysis of Computing Systems
- Volume:
- 6
- Issue:
- 3
- ISSN:
- 2476-1249
- Page Range / eLocation ID:
- 1 to 28
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3570613
