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1. Learning on the Rings: Self-Supervised 3D Finger Motion Tracking Using Wearable Sensors

   https://doi.org/10.1145/3534587  

   Zhou, Hao; Lu, Taiting; Liu, Yilin; Zhang, Shijia; Gowda, Mahanth (July 2022, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   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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2. On the Security of Bluetooth Low Energy in Two Consumer Wearable Heart Rate Monitors/Sensing Devices

   https://doi.org/10.3390/s22030988  

   Kurt Peker, Yeṣem; Bello, Gabriel; Perez, Alfredo J. (February 2022, Sensors)

   Since its inception in 2013, Bluetooth Low Energy (BLE) has become the standard for short-distance wireless communication in many consumer devices, as well as special-purpose devices. In this study, we analyze the security features available in Bluetooth LE standards and evaluate the features implemented in two BLE wearable devices (a Fitbit heart rate wristband and a Polar heart rate chest wearable) and a BLE keyboard to explore which security features in the BLE standards are implemented in the devices. In this study, we used the ComProbe Bluetooth Protocol Analyzer, along with the ComProbe software to capture the BLE traffic of these three devices. We found that even though the standards provide security mechanisms, because the Bluetooth Special Interest Group does not require that manufacturers fully comply with the standards, some manufacturers fail to implement proper security mechanisms. The circumvention of security in Bluetooth devices could leak private data that could be exploited by rogue actors/hackers, thus creating security, privacy, and, possibly, safety issues for consumers and the public. We propose the design of a Bluetooth Security Facts Label (BSFL) to be included on a Bluetooth/BLE enabled device’s commercial packaging and conclude that there should be better mechanisms for informing users about the security and privacy provisions of the devices they acquire and use and to educate the public on protection of their privacy when buying a connected device. 

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3. A Mobile App-Based Indoor Mobility Detection Approach Using Bluetooth Signal Strength

   https://doi.org/10.1109/ICNC59896.2024.10556068  

   Fuad, Muztaba; Smith, Anthony; Deb, Debzani (February 2024, IEEE)

   In an indoor space, determining a person's mobility patterns has research significance and applicability in real-world scenarios. When mobility patterns are determined, layout optimization can be implemented in indoor spaces to improve efficiency. This research aimed to determine a person's path using Received Signal Strength Indicator (RSSI) data collected from Bluetooth-enabled mobile devices. Mobile app-based mobility detection using Bluetooth RSSI has the advantage of low cost and easy implementation. The research methodology involves developing a Bluetooth RSSI mobility application system to determine the path of a moving mobile device using a vectorized algorithm. The paper presents challenges in creating such a software system, its architecture, the data collection and analysis process, and the results of mobility detection. This research shows that Bluetooth-enabled mobile devices and Bluetooth RSSI data can be used to determine the path in an indoor space with workable accuracy. 

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4. Thermo-osmotic ionogel enabled high-efficiency harvesting of low-grade heat

   https://doi.org/10.1039/D1TA01836A  

   Li, Wei; Liu, Yuchen; Zhang, Zimeng; Liu, Ruochen; Qiu, Jingjing; Wang, Shiren (July 2021, Journal of Materials Chemistry A)

   null (Ed.)

   Low efficiency in recovering low-grade heat remains unresolved despite decades of attempts. In this research, we designed and fabricated a novel thermo-osmotic ionogel (TOI) composite to recover low-grade heat to generate electric power through a thermo-induced ion gradient and selective ion diffusion. The TOI composite was assembled with a crystalline ionogel (polymer-confined LiNO 3 –3H 2 O) film, ion selective membrane, and hydrogel film. With a 90 °C heat supply, the single TOI composite produced a high open-circuit voltage of 0.52 V, a differential thermal voltage of ∼26 mV K −1 , a peak power density of 0.4 W m −2 , and a ground-breaking peak energy conversion efficiency of 11.17%. Eight pieces of such a TOI composite were connected in series, demonstrating an open-circuit voltage of 3.25 volts. Such a TOI system was also demonstrated to harvest body temperature for powering a LED, opening numerous opportunities in powering wearable devices. 

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5. A 65nm Resonant Electro-Quasistatic 5-240uW Human Whole-Body Powering and 2.19uW Communication SoC with Automatic Maximum Resonant Power Tracking

   https://doi.org/10.1109/CICC51472.2021.9431456  

   Modak, Nirmoy; Das, Debayan; Nath, Mayukh; Chatterjee, Baibhab; Gaurav Kumar, K; Maity, Shovan; Sen, Shreyas (April 2021, 2021 IEEE Custom Integrated Circuits Conference (CICC))

   Applications like Connected Healthcare through physiological signal monitoring and Secure Authentication using wearable keys can benefit greatly from battery-less operation. Low power communication along with energy harvesting is critical to sustain such perpetual battery-less operation. Previous studies have used techniques such as Tribo-Electric, Piezo-Electric, RF energy harvesting for Body Area Network devices, but they are restricted to on-body node placements. Human body channel is known to be a promising alternative to wireless radio wave communication for low power operation [1-4], through Human Body Communication, as well as very recently as a medium for power transfer through body coupled power transfer [5]. However, channel length (L) dependency of the received power makes it inefficient for L>40cm. There have also been a few studies for low power communication through the human body, but none of them could provide sustainable battery-less operation. In this paper, we utilize Resonant Electro Quasi-Static Human Body Communication (Res-EQS HBC) with Maximum Resonance Power Tracking (MRPT) to enable channel length independent whole-body communication and powering (Fig.1). We design the first system to simultaneously transfer Power and Data between a HUB device and a wearable through the human body to enable battery-less operation. Measurement results show 240uW, 28uW and 5uW power transfer through the body in a MachineMachine (large devices with strong ground connection) Tabletop (small devices kept on a table, as in [5]) and Wearable-Wearable (small form factor battery operated devices) scenario respectively, independent of body channel length, while enabling communication with a power consumption of only 2.19uW. This enables >25x more power transfer with >100x more efficiency compared to [5] for Tabletop and 100cm Body distance by utilizing the benefits of EQS. The MRPT loop automatically tracks device and posture dependent resonance point changes to maximize power transfer in all cases. 

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