This paper introduces MultiMesh, a multi-subject 3D human mesh construction system based on commodity WiFi. Our system can reuse commodity WiFi devices in the environment and is capable of working in non-line-of-sight (NLoS) conditions compared with the traditional computer vision-based approach. Specifically, we leverage an L-shaped antenna array to generate the two-dimensional angle of arrival (2D AoA) of reflected signals for subject separation in the physical space. We further leverage the angle of departure and time of flight of the signal to enhance the resolvability for precise separation of close subjects. Then we exploit information from various signal dimensions to mitigate the interference of indirect reflections according to different signal propagation paths. Moreover, we employ the continuity of human movement in the spatial-temporal domain to track weak reflected signals of faraway subjects. Finally, we utilize a deep learning model to digitize 2D AoA images of each subject into the 3D human mesh. We conducted extensive experiments in real-world multi-subject scenarios under various environments to evaluate the performance of our system. For example, we conduct experiments with occlusion and perform human mesh construction for different distances between two subjects and different distances between subjects and WiFi devices. The results show that MultiMesh can accurately construct 3D human meshes for multiple users with an average vertex error of 4cm. The evaluations also demonstrate that our system could achieve comparable performance for unseen environments and people. Moreover, we also evaluate the accuracy of spatial information extraction and the performance of subject detection. These evaluations demonstrate the robustness and effectiveness of our system.
本文介绍了基于商用 WiFi 的多主体 3D 人体网格构建系统 MultiMesh。与传统的基于计算机视觉的方法相比，我们的系统可以重复使用环境中的商用 WiFi 设备，并且能够在非视线（NLoS）条件下工作。具体来说，我们利用 L 型天线阵列生成反射信号的二维到达角（2D AoA），以便在物理空间中进行主体分离。我们进一步利用信号的离去角和飞行时间来提高精确分离近距离物体的分辨率。然后，我们利用各种信号维度的信息，根据不同的信号传播路径来减轻间接反射的干扰。此外，我们还利用时空域中人体运动的连续性来跟踪远处主体的微弱反射信号。最后，我们利用深度学习模型将每个受试者的 2D AoA 图像数字化为 3D 人体网格。我们在各种环境下的真实世界多主体场景中进行了大量实验，以评估我们系统的性能。例如，我们进行了遮挡实验，并在两个受试者之间的不同距离以及受试者与 WiFi 设备之间的不同距离下进行了人体网格构建。结果表明，MultiMesh 可以为多个用户准确构建三维人体网格，平均顶点误差为 4 厘米。评估结果还表明，我们的系统可以在不可见的环境和人物中实现相当的性能。此外，我们还评估了空间信息提取的准确性和主体检测的性能。这些评估证明了我们系统的鲁棒性和有效性。
{"title":"Multi-Subject 3D Human Mesh Construction Using Commodity WiFi","authors":"Yichao Wang, Yili Ren, Jie Yang","doi":"10.1145/3643504","DOIUrl":"https://doi.org/10.1145/3643504","url":null,"abstract":"This paper introduces MultiMesh, a multi-subject 3D human mesh construction system based on commodity WiFi. Our system can reuse commodity WiFi devices in the environment and is capable of working in non-line-of-sight (NLoS) conditions compared with the traditional computer vision-based approach. Specifically, we leverage an L-shaped antenna array to generate the two-dimensional angle of arrival (2D AoA) of reflected signals for subject separation in the physical space. We further leverage the angle of departure and time of flight of the signal to enhance the resolvability for precise separation of close subjects. Then we exploit information from various signal dimensions to mitigate the interference of indirect reflections according to different signal propagation paths. Moreover, we employ the continuity of human movement in the spatial-temporal domain to track weak reflected signals of faraway subjects. Finally, we utilize a deep learning model to digitize 2D AoA images of each subject into the 3D human mesh. We conducted extensive experiments in real-world multi-subject scenarios under various environments to evaluate the performance of our system. For example, we conduct experiments with occlusion and perform human mesh construction for different distances between two subjects and different distances between subjects and WiFi devices. The results show that MultiMesh can accurately construct 3D human meshes for multiple users with an average vertex error of 4cm. The evaluations also demonstrate that our system could achieve comparable performance for unseen environments and people. Moreover, we also evaluate the accuracy of spatial information extraction and the performance of subject detection. These evaluations demonstrate the robustness and effectiveness of our system.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"14 3","pages":"23:1-23:25"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140260952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wenqiang Chen, Shupei Lin, Zhencan Peng, Farshid Salemi Parizi, Seongkook Heo, Shwetak Patel, Wojciech Matusik, Wei Zhao, Jack Stankovic
Knowing the object grabbed by a hand can offer essential contextual information for interaction between the human and the physical world. This paper presents a novel system, ViObject, for passive object recognition that uses accelerometer and gyroscope sensor data from commodity smartwatches to identify untagged everyday objects. The system relies on the vibrations caused by grabbing objects and does not require additional hardware or human effort. ViObject's ability to recognize objects passively can have important implications for a wide range of applications, from smart home automation to healthcare and assistive technologies. In this paper, we present the design and implementation of ViObject, to address challenges such as motion interference, different object-touching positions, different grasp speeds/pressure, and model customization to new users and new objects. We evaluate the system's performance using a dataset of 20 objects from 20 participants and show that ViObject achieves an average accuracy of 86.4%. We also customize models for new users and new objects, achieving an average accuracy of 90.1%. Overall, ViObject demonstrates a novel technology concept of passive object recognition using commodity smartwatches and opens up new avenues for research and innovation in this area.
{"title":"ViObject: Harness Passive Vibrations for Daily Object Recognition with Commodity Smartwatches","authors":"Wenqiang Chen, Shupei Lin, Zhencan Peng, Farshid Salemi Parizi, Seongkook Heo, Shwetak Patel, Wojciech Matusik, Wei Zhao, Jack Stankovic","doi":"10.1145/3643547","DOIUrl":"https://doi.org/10.1145/3643547","url":null,"abstract":"Knowing the object grabbed by a hand can offer essential contextual information for interaction between the human and the physical world. This paper presents a novel system, ViObject, for passive object recognition that uses accelerometer and gyroscope sensor data from commodity smartwatches to identify untagged everyday objects. The system relies on the vibrations caused by grabbing objects and does not require additional hardware or human effort. ViObject's ability to recognize objects passively can have important implications for a wide range of applications, from smart home automation to healthcare and assistive technologies. In this paper, we present the design and implementation of ViObject, to address challenges such as motion interference, different object-touching positions, different grasp speeds/pressure, and model customization to new users and new objects. We evaluate the system's performance using a dataset of 20 objects from 20 participants and show that ViObject achieves an average accuracy of 86.4%. We also customize models for new users and new objects, achieving an average accuracy of 90.1%. Overall, ViObject demonstrates a novel technology concept of passive object recognition using commodity smartwatches and opens up new avenues for research and innovation in this area.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"42 8","pages":"5:1-5:26"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140261661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Electrodermal activity (EDA) is a physiological signal that can be used to infer humans' affective states and stress levels. EDA can nowadays be monitored using unobtrusive wearable devices, such as smartwatches, and leveraged in personal informatics systems. A still largely uncharted issue concerning EDA is the impact on real applications of potential differences observable on signals measured concurrently on the left and right side of the human body. This phenomenon, called lateralization, originates from the distinct functions that the brain's left and right hemispheres exert on EDA. In this work, we address this issue by examining the impact of EDA lateralization in two classification tasks: a cognitive load recognition task executed in the lab and a sleep monitoring task in a real-world setting. We implement a machine learning pipeline to compare the performance obtained on both classification tasks using EDA data collected from the left and right sides of the body. Our results show that using EDA from the side that is not associated with the specific hemisphere activation leads to a significant decline in performance for the considered classification tasks. This finding highlights that researchers and practitioners relying on EDA data should consider possible EDA lateralization effects when deciding on sensor placement.
{"title":"Lateralization Effects in Electrodermal Activity Data Collected Using Wearable Devices","authors":"Leonardo Alchieri, Nouran Abdalazim, Lidia Alecci, Shkurta Gashi, M. Gjoreski, Silvia Santini","doi":"10.1145/3643541","DOIUrl":"https://doi.org/10.1145/3643541","url":null,"abstract":"Electrodermal activity (EDA) is a physiological signal that can be used to infer humans' affective states and stress levels. EDA can nowadays be monitored using unobtrusive wearable devices, such as smartwatches, and leveraged in personal informatics systems. A still largely uncharted issue concerning EDA is the impact on real applications of potential differences observable on signals measured concurrently on the left and right side of the human body. This phenomenon, called lateralization, originates from the distinct functions that the brain's left and right hemispheres exert on EDA. In this work, we address this issue by examining the impact of EDA lateralization in two classification tasks: a cognitive load recognition task executed in the lab and a sleep monitoring task in a real-world setting. We implement a machine learning pipeline to compare the performance obtained on both classification tasks using EDA data collected from the left and right sides of the body. Our results show that using EDA from the side that is not associated with the specific hemisphere activation leads to a significant decline in performance for the considered classification tasks. This finding highlights that researchers and practitioners relying on EDA data should consider possible EDA lateralization effects when deciding on sensor placement.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"65 3","pages":"2:1-2:30"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140261501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuqi Su, Fusang Zhang, Kai Niu, Tianben Wang, Beihong Jin, Zhi Wang, Yalan Jiang, Daqing Zhang, Lili Qiu, Jie Xiong
Contactless acoustic sensing has been actively exploited in the past few years to enable a large range of applications, ranging from fine-grained vital sign monitoring to coarse-grained human tracking. However, existing acoustic sensing systems mainly work on smartphone or smart speaker platforms. In this paper, we envision an exciting new acoustic sensing platform, i.e., car cabin which is inherently embedded with a large number of speakers and microphones. We propose the new concept of distributed acoustic sensing and develop novel designs leveraging the unique characteristics of rich multi-path in car cabin to enable fine-grained sensing even when the primary reflection is totally blocked. By using child presence detection as the application example, we show that child presence can be detected through body motions or even subtle breath (when the child is sleeping or in coma) at all locations in the cabin without any blind spots. We further show that the proposed system can robustly work in different car cabins, achieving an average detection accuracy of 97% and a false alarm rate always below 2% under different scenarios including those challenging ones such as rear-facing seat blockage. We believe the proposed distributed sensing modality in car cabin pushes acoustic sensing one big step towards real-life adoption.
{"title":"Embracing Distributed Acoustic Sensing in Car Cabin for Children Presence Detection","authors":"Yuqi Su, Fusang Zhang, Kai Niu, Tianben Wang, Beihong Jin, Zhi Wang, Yalan Jiang, Daqing Zhang, Lili Qiu, Jie Xiong","doi":"10.1145/3643548","DOIUrl":"https://doi.org/10.1145/3643548","url":null,"abstract":"Contactless acoustic sensing has been actively exploited in the past few years to enable a large range of applications, ranging from fine-grained vital sign monitoring to coarse-grained human tracking. However, existing acoustic sensing systems mainly work on smartphone or smart speaker platforms. In this paper, we envision an exciting new acoustic sensing platform, i.e., car cabin which is inherently embedded with a large number of speakers and microphones. We propose the new concept of distributed acoustic sensing and develop novel designs leveraging the unique characteristics of rich multi-path in car cabin to enable fine-grained sensing even when the primary reflection is totally blocked. By using child presence detection as the application example, we show that child presence can be detected through body motions or even subtle breath (when the child is sleeping or in coma) at all locations in the cabin without any blind spots. We further show that the proposed system can robustly work in different car cabins, achieving an average detection accuracy of 97% and a false alarm rate always below 2% under different scenarios including those challenging ones such as rear-facing seat blockage. We believe the proposed distributed sensing modality in car cabin pushes acoustic sensing one big step towards real-life adoption.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"14 3","pages":"16:1-16:28"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140262283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lupeng Zhang, Pingchuan Wang, Minhao Cui, Jingwen Wei, Yu Tian, Jingchi Zhang, Jie Xiong, Lei Wang
Underwater Visible Light Communication (UVLC) is promising due to its relatively strong penetration capability in water and large frequency bandwidth. Visible Light Communication (VLC) is also considered a safer wireless communication paradigm as light signals can be constrained within the area of interest with obstacles such as walls, reducing the chance of potential attack. However, this intuitional security assumption is not true anymore in underwater environment. Recent research shows that the eavesdropping risk of UVLC is more severe than we thought, attributed to the divergence and scattering effects of light beams in water. In this paper, we harness the dynamic nature of underwater environments as a true random resource to extract symmetric keys for UVLC. Specifically, the proposed AquaKey system incorporates instantaneous bidirectional channel probing, computation of relative channel characteristics, and an environment-adaptive quantization algorithm. The above design addresses unique challenges caused by the dynamic underwater environment, including self-interference, high-frequency disturbance, and mismatch, ensuring the practicality and applicability of AquaKey. Additionally, AquaKey has negligible impact on communication and has no effect on the illumination function. Through extensive real-world experiments, we show that AquaKey can achieve reliable key extraction with cheap hardware, generating a 512-bit key in just 0.5-1 seconds.
{"title":"AquaKey: Exploiting the Randomness of the Underwater Visible Light Communication Channel for Key Extraction","authors":"Lupeng Zhang, Pingchuan Wang, Minhao Cui, Jingwen Wei, Yu Tian, Jingchi Zhang, Jie Xiong, Lei Wang","doi":"10.1145/3643557","DOIUrl":"https://doi.org/10.1145/3643557","url":null,"abstract":"Underwater Visible Light Communication (UVLC) is promising due to its relatively strong penetration capability in water and large frequency bandwidth. Visible Light Communication (VLC) is also considered a safer wireless communication paradigm as light signals can be constrained within the area of interest with obstacles such as walls, reducing the chance of potential attack. However, this intuitional security assumption is not true anymore in underwater environment. Recent research shows that the eavesdropping risk of UVLC is more severe than we thought, attributed to the divergence and scattering effects of light beams in water. In this paper, we harness the dynamic nature of underwater environments as a true random resource to extract symmetric keys for UVLC. Specifically, the proposed AquaKey system incorporates instantaneous bidirectional channel probing, computation of relative channel characteristics, and an environment-adaptive quantization algorithm. The above design addresses unique challenges caused by the dynamic underwater environment, including self-interference, high-frequency disturbance, and mismatch, ensuring the practicality and applicability of AquaKey. Additionally, AquaKey has negligible impact on communication and has no effect on the illumination function. Through extensive real-world experiments, we show that AquaKey can achieve reliable key extraction with cheap hardware, generating a 512-bit key in just 0.5-1 seconds.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"22 S2","pages":"28:1-28:25"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140262503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ear wearables (earables) are emerging platforms that are broadly adopted in various applications. There is an increasing demand for robust earables authentication because of the growing amount of sensitive information and the IoT devices that the earable could access. Traditional authentication methods become less feasible due to the limited input interface of earables. Nevertheless, the rich head-related sensing capabilities of earables can be exploited to capture human biometrics. In this paper, we propose EarSlide, an earable biometric authentication system utilizing the advanced sensing capacities of earables and the distinctive features of acoustic fingerprints when users slide their fingers on the face. It utilizes the inward-facing microphone of the earables and the face-ear channel of the ear canal to reliably capture the acoustic fingerprint. In particular, we study the theory of friction sound and categorize the characteristics of the acoustic fingerprints into three representative classes, pattern-class, ridge-groove-class, and coupling-class. Different from traditional fingerprint authentication only utilizes 2D patterns, we incorporate the 3D information in acoustic fingerprint and indirectly sense the fingerprint for authentication. We then design representative sliding gestures that carry rich information about the acoustic fingerprint while being easy to perform. It then extracts multi-class acoustic fingerprint features to reflect the inherent acoustic fingerprint characteristic for authentication. We also adopt an adaptable authentication model and a user behavior mitigation strategy to effectively authenticate legit users from adversaries. The key advantages of EarSlide are that it is resistant to spoofing attacks and its wide acceptability. Our evaluation of EarSlide in diverse real-world environments with intervals over one year shows that EarSlide achieves an average balanced accuracy rate of 98.37% with only one sliding gesture.
{"title":"EarSlide: a Secure Ear Wearables Biometric Authentication Based on Acoustic Fingerprint","authors":"Zi Wang, Yilin Wang, Jie Yang","doi":"10.1145/3643515","DOIUrl":"https://doi.org/10.1145/3643515","url":null,"abstract":"Ear wearables (earables) are emerging platforms that are broadly adopted in various applications. There is an increasing demand for robust earables authentication because of the growing amount of sensitive information and the IoT devices that the earable could access. Traditional authentication methods become less feasible due to the limited input interface of earables. Nevertheless, the rich head-related sensing capabilities of earables can be exploited to capture human biometrics. In this paper, we propose EarSlide, an earable biometric authentication system utilizing the advanced sensing capacities of earables and the distinctive features of acoustic fingerprints when users slide their fingers on the face. It utilizes the inward-facing microphone of the earables and the face-ear channel of the ear canal to reliably capture the acoustic fingerprint. In particular, we study the theory of friction sound and categorize the characteristics of the acoustic fingerprints into three representative classes, pattern-class, ridge-groove-class, and coupling-class. Different from traditional fingerprint authentication only utilizes 2D patterns, we incorporate the 3D information in acoustic fingerprint and indirectly sense the fingerprint for authentication. We then design representative sliding gestures that carry rich information about the acoustic fingerprint while being easy to perform. It then extracts multi-class acoustic fingerprint features to reflect the inherent acoustic fingerprint characteristic for authentication. We also adopt an adaptable authentication model and a user behavior mitigation strategy to effectively authenticate legit users from adversaries. The key advantages of EarSlide are that it is resistant to spoofing attacks and its wide acceptability. Our evaluation of EarSlide in diverse real-world environments with intervals over one year shows that EarSlide achieves an average balanced accuracy rate of 98.37% with only one sliding gesture.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"27 19","pages":"24:1-24:29"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140262691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mobile app light and dark modes offer improved usability within different contexts (e.g., dark mode for easier night reading). Yet, little research has investigated the prevalence of light and dark modes across platforms, the intricacies of UI color changes, and challenges in the design and development process. Our investigation focused on comparing light and dark mode designs. We carried out a manual inspection of 120 popular Android and iOS apps to find that only 55% (Android) and 48% (iOS) of the apps included any modes. We also found significant variability in how many UI elements changed between modes. We interviewed 15 designers and developers to understand the creative process, motivations for design decisions, and what challenges exist in the processes that affect alternative mode implementation. We identified several issues that HCI researchers are equipped to solve, ultimately improving support for mobile app creators looking to implement dark modes (and beyond) for increased mobile usability in different contexts.
{"title":"Light and Dark Mode: A Comparison Between Android and iOS App UI Modes and Interviews with App Designers and Developers","authors":"Sarah Andrew, Chelsea Bishop, Garreth W. Tigwell","doi":"10.1145/3643539","DOIUrl":"https://doi.org/10.1145/3643539","url":null,"abstract":"Mobile app light and dark modes offer improved usability within different contexts (e.g., dark mode for easier night reading). Yet, little research has investigated the prevalence of light and dark modes across platforms, the intricacies of UI color changes, and challenges in the design and development process. Our investigation focused on comparing light and dark mode designs. We carried out a manual inspection of 120 popular Android and iOS apps to find that only 55% (Android) and 48% (iOS) of the apps included any modes. We also found significant variability in how many UI elements changed between modes. We interviewed 15 designers and developers to understand the creative process, motivations for design decisions, and what challenges exist in the processes that affect alternative mode implementation. We identified several issues that HCI researchers are equipped to solve, ultimately improving support for mobile app creators looking to implement dark modes (and beyond) for increased mobile usability in different contexts.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"30 12","pages":"32:1-32:23"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140262811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A spinal cord injury (SCI) typically results in a sudden change to an individual's motor function. People's adoption of technology soon after a severe SCI is crucial, since they must relearn most technology interactions to adjust to their new physical abilities and regain independence. This study examines how individuals adopt technologies soon after sustaining a severe SCI. By qualitatively analyzing the perspectives of ten rehabilitation clinicians, three individuals who recently sustained an SCI, and two of those participants' family members, we surfaced a spectrum of individuals' motivations to adopt technology post-injury and highlight the challenges they face to adopt technology. Our findings highlight the need to incorporate the holistic experience---including technology literacy, perception of support, and acceptance of the "new-normal"---in technology design for individuals who have a sudden change to motor functions. Our findings show that technology adoption is a critical component for the overall adjustment of post-SCI life. Finally, we use the extended version of the Technology Acceptance Model (TAM) to make recommendations for more inclusive assistive design.
{"title":"Investigating Technology Adoption Soon After Sustaining a Spinal Cord Injury","authors":"Tamanna Motahar, Jason Wiese","doi":"10.1145/3643507","DOIUrl":"https://doi.org/10.1145/3643507","url":null,"abstract":"A spinal cord injury (SCI) typically results in a sudden change to an individual's motor function. People's adoption of technology soon after a severe SCI is crucial, since they must relearn most technology interactions to adjust to their new physical abilities and regain independence. This study examines how individuals adopt technologies soon after sustaining a severe SCI. By qualitatively analyzing the perspectives of ten rehabilitation clinicians, three individuals who recently sustained an SCI, and two of those participants' family members, we surfaced a spectrum of individuals' motivations to adopt technology post-injury and highlight the challenges they face to adopt technology. Our findings highlight the need to incorporate the holistic experience---including technology literacy, perception of support, and acceptance of the \"new-normal\"---in technology design for individuals who have a sudden change to motor functions. Our findings show that technology adoption is a critical component for the overall adjustment of post-SCI life. Finally, we use the extended version of the Technology Acceptance Model (TAM) to make recommendations for more inclusive assistive design.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"15 1","pages":"14:1-14:24"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140263236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. Miao, Zhenyu Yan, HU Wen, Chaojie Gu, Yiu Chau, Rui Tan, Qi Lin
Secure device pairing is important to wearables. Existing solutions either degrade usability due to the need of specific actions like shaking, or they lack universality due to the need of dedicated hardware like electrocardiogram sensors. This paper proposes TouchKey, a symmetric key generation scheme that exploits the skin electric potential (SEP) induced by powerline electromagnetic radiation. The SEP is ubiquitously accessible indoors with analog-to-digital converters widely available on Internet of Things devices. Our measurements show that the SEP has high randomness and the SEPs measured at two close locations on the same human body are similar. Extensive experiments show that TouchKey achieves a high key generation rate of 345 bit/s and an average success rate of 99.29%. Under a range of adversary models including active and passive attacks, TouchKey shows a low false acceptance rate of 0.86%, which outperforms existing solutions. Besides, the overall execution time and energy usage are 0.44 s and 2.716 mJ, which make it suitable for resource-constrained devices.
安全的设备配对对可穿戴设备很重要。现有的解决方案要么因为需要特定的动作(如摇动)而降低可用性
{"title":"TouchKey: Touch to Generate Symmetric Keys by Skin Electric Potentials Induced by Powerline Radiation","authors":"Y. Miao, Zhenyu Yan, HU Wen, Chaojie Gu, Yiu Chau, Rui Tan, Qi Lin","doi":"10.1145/3596264","DOIUrl":"https://doi.org/10.1145/3596264","url":null,"abstract":"Secure device pairing is important to wearables. Existing solutions either degrade usability due to the need of specific actions like shaking, or they lack universality due to the need of dedicated hardware like electrocardiogram sensors. This paper proposes TouchKey, a symmetric key generation scheme that exploits the skin electric potential (SEP) induced by powerline electromagnetic radiation. The SEP is ubiquitously accessible indoors with analog-to-digital converters widely available on Internet of Things devices. Our measurements show that the SEP has high randomness and the SEPs measured at two close locations on the same human body are similar. Extensive experiments show that TouchKey achieves a high key generation rate of 345 bit/s and an average success rate of 99.29%. Under a range of adversary models including active and passive attacks, TouchKey shows a low false acceptance rate of 0.86%, which outperforms existing solutions. Besides, the overall execution time and energy usage are 0.44 s and 2.716 mJ, which make it suitable for resource-constrained devices.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"40 1","pages":"1 - 21"},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81731458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The last few decades have witnessed an emerging trend of wearable soft sensors; however, there are important signal-processing challenges for soft sensors that still limit their practical deployment. They are error-prone when displaced, resulting in significant deviations from their ideal sensor output. In this work, we propose a novel prototype that integrates an elbow pad with a sparse network of soft sensors. Our prototype is fully bio-compatible, stretchable, and wearable. We develop a learning-based method to predict the elbow orientation angle and achieve an average tracking error of 9.82 degrees for single-user multi-motion experiments. With transfer learning, our method achieves the average tracking errors of 10.98 degrees and 11.81 degrees across different motion types and users, respectively. Our core contributions lie in a solution that realizes robust and stable human joint motion tracking across different device displacements.
{"title":"DisPad: Flexible On-Body Displacement of Fabric Sensors for Robust Joint-Motion Tracking","authors":"Xiaowei Chen, Xiao Jiang, Jia-Qi Fang, Shihui Guo, Juncong Lin, Minghong Liao, Guoliang Luo, Hongbo Fu","doi":"10.48550/arXiv.2301.06249","DOIUrl":"https://doi.org/10.48550/arXiv.2301.06249","url":null,"abstract":"The last few decades have witnessed an emerging trend of wearable soft sensors; however, there are important signal-processing challenges for soft sensors that still limit their practical deployment. They are error-prone when displaced, resulting in significant deviations from their ideal sensor output. In this work, we propose a novel prototype that integrates an elbow pad with a sparse network of soft sensors. Our prototype is fully bio-compatible, stretchable, and wearable. We develop a learning-based method to predict the elbow orientation angle and achieve an average tracking error of 9.82 degrees for single-user multi-motion experiments. With transfer learning, our method achieves the average tracking errors of 10.98 degrees and 11.81 degrees across different motion types and users, respectively. Our core contributions lie in a solution that realizes robust and stable human joint motion tracking across different device displacements.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"8 1","pages":"5:1-5:27"},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74434407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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