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1. Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table

   https://doi.org/10.1371/journal.pone.0286898  

   White, James W; Finnegan, Olivia L; Tindall, Nick; Nelakuditi, Srihari; Brown, David E; Pate, Russell R; Welk, Gregory J; de_Zambotti, Massimiliano; Ghosal, Rahul; Wang, Yuan; et al (March 2024, PLOS ONE)

   Yamada, Yosuke (Ed.)

   The purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n = 10 ActiGraph wGT3X-BT, n = 10 Apple Watch Series 7, n = 10 Garmin Vivoactive 4S, and n = 10 Fitbit Sense) were compared to reference accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested inter-device reliability. Pearson product moment, Lin’s concordance correlation coefficient (CCC), absolute error, mean bias, and equivalence testing were calculated to assess the validity between the raw estimates from the devices and the reference metric. Estimates from Apple, ActiGraph, Garmin, and Fitbit were reliable, with ICCs = 0.99, 0.97, 0.88, and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the reference CCCs = 0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC = 0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors = 16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error = 32.5mg compared to the reference. ActiGraph produced the lowest mean bias 0.0mg (95%CI = -40.0, 41.0). Equivalence testing revealed raw accelerometry data from all devices were not statistically significantly within the equivalence bounds of the shaker speed. Findings from this study provide evidence that raw accelerometry data from Apple, Garmin, and Fitbit devices can be used to reliably estimate movement; however, no estimates were statistically significantly equivalent to the reference. Future studies could explore device-agnostic and harmonization methods for estimating physical activity using the raw accelerometry signals from the consumer wearables studied herein. 

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2. UniverSense: IoT Device Pairing through Heterogeneous Sensing Signals

   https://doi.org/10.1145/3177102.3177108  

   Pan, Shijia; Ruiz, Carlos; Han, Jun; Bannis, Adeola; Tague, Patrick; Noh, Hae Young; Zhang, Pei (February 2018, 19th Intl Workshop on Mobile Computing Systems and Applications (HotMobile))

   Easily establishing pairing between Internet-of-Things (IoT) devices is important for fast deployment in many smart home scenarios. Traditional pairing methods, including passkey, QR code, and RFID, often require specific user interfaces, surface’s shape/material, or additional tags/readers. The growing number of low-resource IoT devices without an interface may not meet these requirements, which makes their pairing a challenge. On the other hand, these devices often already have sensors embedded for sensing tasks, such as inertial sensors. These sensors can be used for limited user interaction with the devices, but are not suitable for pairing on their own. In this paper, we present UniverSense, an alternative pairing method between low-resource IoT devices with an inertial sensor and a more powerful networked device equipped with a camera. To establish pairing between them, the user moves the low-resource IoT device in front of the camera. Both the camera and the on-device sensors capture the physical motion of the low-resource device. UniverSense converts these signals into a common state-space to generate fingerprints for pairing. We conduct real-world experiments to evaluate UniverSense and it achieves an F1 score of 99.9% in experiments carried out by five participants. 

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3. GazePair: Efficient Pairing of Augmented Reality Devices Using Gaze Tracking

   https://doi.org/10.1109/TMC.2023.3255841  

   Corbett, Matthew; Shang, Jiacheng; Ji, Bo (March 2023, IEEE Transactions on Mobile Computing)

   As Augmented Reality (AR) devices become more prevalent and commercially viable, the need for quick, efficient, and secure schemes for pairing these devices has become more pressing. Current methods to securely exchange holograms require users to send this information through large data centers, creating security and privacy concerns. Existing techniques to pair these devices on a local network and share information fall short in terms of usability and scalability. These techniques either require hardware not available on AR devices, intricate physical gestures, removal of the device from the head, do not scale to multiple pairing partners, or rely on methods with low entropy to create encryption keys. To that end, we propose a novel pairing system, called GazePair, that improves on all existing local pairing techniques by creating an efficient, effective, and intuitive pairing protocol. GazePair uses eye gaze tracking and a spoken key sequence cue (KSC) to generate identical, independently generated symmetric encryption keys with 64 bits of entropy. GazePair also achieves improvements in pairing success rates and times over current methods. Additionally, we show that GazePair can extend to multiple users. Finally, we assert that GazePair can be used on any Mixed Reality (MR) device equipped with eye gaze tracking. 

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4. Do You Feel What I Hear? Enabling Autonomous IoT Device Pairing Using Different Sensor Types

   https://doi.org/10.1109/sp.2018.00041  

   Han, Jun; Chung, Albert Jin; Sinha, Manal Kumar; Harishankar, Madhumitha; Pan, Shijia; Noh, Hae Young; Zhang, Pei; Tague, Patrick (May 2018, 39th IEEE Symposium on Security and Privacy (Oakland 2018))

   Context-based pairing solutions increase the usability of IoT device pairing by eliminating any human involvement in the pairing process. This is possible by utilizing on-board sensors (with same sensing modalities) to capture a common physical context (e.g., ambient sound via each device’s microphone). However, in a smart home scenario, it is impractical to assume that all devices will share a common sensing modality. For example, a motion detector is only equipped with an infrared sensor while Amazon Echo only has microphones. In this paper, we develop a new context-based pairing mechanism called Perceptio that uses time as the common factor across differing sensor types. By focusing on the event timing, rather than the specific event sensor data, Perceptio creates event fingerprints that can be matched across a variety of IoT devices. We propose Perceptio based on the idea that devices co-located within a physically secure boundary (e.g., single family house) can observe more events in common over time, as opposed to devices outside. Devices make use of the observed contextual information to provide entropy for Perceptio’s pairing protocol. We design and implement Perceptio, and evaluate its effectiveness as an autonomous secure pairing solution. Our implementation demonstrates the ability to sufficiently distinguish between legitimate devices (placed within the boundary) and attacker devices (placed outside) by imposing a threshold on fingerprint similarity. Perceptio demonstrates an average fingerprint similarity of 94.9% between legitimate devices while even a hypothetical impossibly well-performing attacker yields only 68.9% between itself and a valid device. 

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5. Analyzing Interactions in Paired Egocentric Videos

   https://doi.org/10.1109/FG57933.2023.10042654  

   Khatri, Ajeeta; Butler, Zachary; Nwogu, Ifeoma (January 2023, IEEE International Conference on Automatic Face and Gesture Recognition (FG))

   As wearable devices become more popular, ego-centric information recorded with these devices can be used to better understand the behaviors of the wearer and other people the wearer is interacting with. Data such as the voice, head movement, galvanic skin responses (GSR) to measure arousal levels, etc., obtained from such devices can provide a window into the underlying affect of both the wearer and his/her conversant. In this study, we examine the characteristics of two types of dyadic conversations. In one case, the interlocutors discuss a topic on which they agree, while the other situation involves interlocutors discussing a topic on which they disagree, even if they are friends. The range of topics is mostly politically motivated. The egocentric information is collected using a pair of wearable smart glasses for video data and a smart wristband for physiological data, including GSR. Using this data, various features are extracted including the facial expressions of the conversant and the 3D motion from the wearer's camera within the environment - this motion is termed as egomotion. The goal of this work is to investigate whether the nature of a discussion could be better determined either by evaluating the behavior of an individual in the conversation or by evaluating the pairing/coupling of the behaviors of the two people in the conversation. The pairing is accomplished using a modified formulation of the dynamic time warping (DTW) algorithm. A random forest classifier is implemented to evaluate the nature of the interaction (agreement versus disagreement) using individualistic and paired features separately. The study found that in the presence of the limited data used in this work, individual behaviors were slightly more indicative of the type of discussion (85.43% accuracy) than the paired behaviors (83.33% accuracy). 

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