Harish Venugopalan, Z. Din, Trevor Carpenter, Jason Lowe-Power, Samuel T. King, Zubair Shafiq
Mobile app developers often rely on cameras to implement rich features. However, giving apps unfettered access to the mobile camera poses a privacy threat when camera frames capture sensitive information that is not needed for the app's functionality. To mitigate this threat, we present Aragorn, a novel privacy-enhancing mobile camera system that provides fine grained control over what information can be present in camera frames before apps can access them. Aragorn automatically sanitizes camera frames by detecting regions that are essential to an app's functionality and blocking out everything else to protect privacy while retaining app utility. Aragorn can cater to a wide range of camera apps and incorporates knowledge distillation and crowdsourcing to extend robust support to previously unsupported apps. In our evaluations, we see that, with no degradation in utility, Aragorn detects credit cards with 89% accuracy and faces with 100% accuracy in context of credit card scanning and face recognition respectively. We show that Aragorn's implementation in the Android camera subsystem only suffers an average drop of 0.01 frames per second in frame rate. Our evaluations show that the overhead incurred by Aragorn to system performance is reasonable.
{"title":"Aragorn","authors":"Harish Venugopalan, Z. Din, Trevor Carpenter, Jason Lowe-Power, Samuel T. King, Zubair Shafiq","doi":"10.1145/3631406","DOIUrl":"https://doi.org/10.1145/3631406","url":null,"abstract":"Mobile app developers often rely on cameras to implement rich features. However, giving apps unfettered access to the mobile camera poses a privacy threat when camera frames capture sensitive information that is not needed for the app's functionality. To mitigate this threat, we present Aragorn, a novel privacy-enhancing mobile camera system that provides fine grained control over what information can be present in camera frames before apps can access them. Aragorn automatically sanitizes camera frames by detecting regions that are essential to an app's functionality and blocking out everything else to protect privacy while retaining app utility. Aragorn can cater to a wide range of camera apps and incorporates knowledge distillation and crowdsourcing to extend robust support to previously unsupported apps. In our evaluations, we see that, with no degradation in utility, Aragorn detects credit cards with 89% accuracy and faces with 100% accuracy in context of credit card scanning and face recognition respectively. We show that Aragorn's implementation in the Android camera subsystem only suffers an average drop of 0.01 frames per second in frame rate. Our evaluations show that the overhead incurred by Aragorn to system performance is reasonable.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"4 4","pages":"1 - 31"},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437964","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}
E. Yi, Fusang Zhang, Jie Xiong, Kai Niu, Zhiyun Yao, Daqing Zhang
The last few years have witnessed the rapid development of WiFi sensing with a large spectrum of applications enabled. However, existing works mainly leverage the obsolete 802.11n WiFi cards (i.e., Intel 5300 and Atheros AR9k series cards) for sensing. On the other hand, the mainstream WiFi protocols currently in use are 802.11ac/ax and commodity WiFi products on the market are equipped with new-generation WiFi chips such as Broadcom BCM43794 and Qualcomm QCN5054. After conducting some benchmark experiments, we find that WiFi sensing has problems working on these new cards. The new communication features (e.g., MU-MIMO) designed to facilitate data transmissions negatively impact WiFi sensing. Conventional CSI base signals such as CSI amplitude and/or CSI phase difference between antennas which worked well on Intel 5300 802.11n WiFi card may fail on new cards. In this paper, we propose delicate signal processing schemes to make wireless sensing work well on these new WiFi cards. We employ two typical sensing applications, i.e., human respiration monitoring and human trajectory tracking to demonstrate the effectiveness of the proposed schemes. We believe it is critical to ensure WiFi sensing compatible with the latest WiFi protocols and this work moves one important step towards real-life adoption of WiFi sensing.
{"title":"Enabling WiFi Sensing on New-generation WiFi Cards","authors":"E. Yi, Fusang Zhang, Jie Xiong, Kai Niu, Zhiyun Yao, Daqing Zhang","doi":"10.1145/3633807","DOIUrl":"https://doi.org/10.1145/3633807","url":null,"abstract":"The last few years have witnessed the rapid development of WiFi sensing with a large spectrum of applications enabled. However, existing works mainly leverage the obsolete 802.11n WiFi cards (i.e., Intel 5300 and Atheros AR9k series cards) for sensing. On the other hand, the mainstream WiFi protocols currently in use are 802.11ac/ax and commodity WiFi products on the market are equipped with new-generation WiFi chips such as Broadcom BCM43794 and Qualcomm QCN5054. After conducting some benchmark experiments, we find that WiFi sensing has problems working on these new cards. The new communication features (e.g., MU-MIMO) designed to facilitate data transmissions negatively impact WiFi sensing. Conventional CSI base signals such as CSI amplitude and/or CSI phase difference between antennas which worked well on Intel 5300 802.11n WiFi card may fail on new cards. In this paper, we propose delicate signal processing schemes to make wireless sensing work well on these new WiFi cards. We employ two typical sensing applications, i.e., human respiration monitoring and human trajectory tracking to demonstrate the effectiveness of the proposed schemes. We believe it is critical to ensure WiFi sensing compatible with the latest WiFi protocols and this work moves one important step towards real-life adoption of WiFi sensing.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"7 4","pages":"1 - 26"},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437586","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}
Wireless earbuds have been gaining increasing popularity and using them to make phone calls or issue voice commands requires the earbud microphones to pick up human speech. When the speaker is in a noisy environment, speech quality degrades significantly and requires speech enhancement (SE). In this paper, we present ClearSpeech, a novel deep-learning-based SE system designed for wireless earbuds. Specifically, by jointly using the earbud's in-ear and out-ear microphones, we devised a suite of techniques to effectively fuse the two signals and enhance the magnitude and phase of the speech spectrogram. We built an earbud prototype to evaluate ClearSpeech under various settings with data collected from 20 subjects. Our results suggest that ClearSpeech can improve the SE performance significantly compared to conventional approaches using the out-ear microphone only. We also show that ClearSpeech can process user speech in real-time on smartphones.
无线耳塞越来越受欢迎，使用它拨打电话或发出语音命令需要耳塞麦克风拾取人的语音。当说话者处于嘈杂环境中时，语音质量会明显下降，因此需要进行语音增强（SE）。在本文中，我们介绍了 ClearSpeech，这是一种基于深度学习的新型 SE 系统，专为无线耳塞设计。具体来说，通过联合使用耳塞的耳内和耳外麦克风，我们设计了一套技术来有效融合这两个信号，并增强语音频谱图的幅度和相位。我们制作了一个耳塞原型，利用从 20 名受试者那里收集的数据，对 ClearSpeech 在各种设置下的效果进行了评估。结果表明，与只使用耳外麦克风的传统方法相比，ClearSpeech 能显著提高 SE 性能。我们还证明 ClearSpeech 可以在智能手机上实时处理用户语音。
{"title":"ClearSpeech","authors":"Dong Ma, Ting Dang, Ming Ding, Rajesh Balan","doi":"10.1145/3631409","DOIUrl":"https://doi.org/10.1145/3631409","url":null,"abstract":"Wireless earbuds have been gaining increasing popularity and using them to make phone calls or issue voice commands requires the earbud microphones to pick up human speech. When the speaker is in a noisy environment, speech quality degrades significantly and requires speech enhancement (SE). In this paper, we present ClearSpeech, a novel deep-learning-based SE system designed for wireless earbuds. Specifically, by jointly using the earbud's in-ear and out-ear microphones, we devised a suite of techniques to effectively fuse the two signals and enhance the magnitude and phase of the speech spectrogram. We built an earbud prototype to evaluate ClearSpeech under various settings with data collected from 20 subjects. Our results suggest that ClearSpeech can improve the SE performance significantly compared to conventional approaches using the out-ear microphone only. We also show that ClearSpeech can process user speech in real-time on smartphones.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"3 6","pages":"1 - 25"},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437793","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}
Tianyu Zhang, Dongheng Zhang, Guanzhong Wang, Yadong Li, Yang Hu, Qibin sun, Yan Chen
In recent years, decimeter-level accuracy in WiFi indoor localization has become attainable within controlled environments. However, existing methods encounter challenges in maintaining robustness in more complex indoor environments: angle-based methods are compromised by the significant localization errors due to unreliable Angle of Arrival (AoA) estimations, and fingerprint-based methods suffer from performance degradation due to environmental changes. In this paper, we propose RLoc, a learning-based system designed for reliable localization and tracking. The key design principle of RLoc lies in quantifying the uncertainty level arises in the AoA estimation task and then exploiting the uncertainty to enhance the reliability of localization and tracking. To this end, RLoc first manually extracts the underutilized beamwidth feature via signal processing techniques. Then, it integrates the uncertainty quantification into neural network design through Kullback-Leibler (KL) divergence loss and ensemble techniques. Finally, these quantified uncertainties guide RLoc to optimally leverage the diversity of Access Points (APs) and the temporal continuous information of AoAs. Our experiments, evaluating on two datasets gathered from commercial off-the-shelf WiFi devices, demonstrate that RLoc surpasses state-of-the-art approaches by an average of 36.27% in in-domain scenarios and 20.40% in cross-domain scenarios.
{"title":"RLoc","authors":"Tianyu Zhang, Dongheng Zhang, Guanzhong Wang, Yadong Li, Yang Hu, Qibin sun, Yan Chen","doi":"10.1145/3631437","DOIUrl":"https://doi.org/10.1145/3631437","url":null,"abstract":"In recent years, decimeter-level accuracy in WiFi indoor localization has become attainable within controlled environments. However, existing methods encounter challenges in maintaining robustness in more complex indoor environments: angle-based methods are compromised by the significant localization errors due to unreliable Angle of Arrival (AoA) estimations, and fingerprint-based methods suffer from performance degradation due to environmental changes. In this paper, we propose RLoc, a learning-based system designed for reliable localization and tracking. The key design principle of RLoc lies in quantifying the uncertainty level arises in the AoA estimation task and then exploiting the uncertainty to enhance the reliability of localization and tracking. To this end, RLoc first manually extracts the underutilized beamwidth feature via signal processing techniques. Then, it integrates the uncertainty quantification into neural network design through Kullback-Leibler (KL) divergence loss and ensemble techniques. Finally, these quantified uncertainties guide RLoc to optimally leverage the diversity of Access Points (APs) and the temporal continuous information of AoAs. Our experiments, evaluating on two datasets gathered from commercial off-the-shelf WiFi devices, demonstrate that RLoc surpasses state-of-the-art approaches by an average of 36.27% in in-domain scenarios and 20.40% in cross-domain scenarios.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"12 3","pages":"1 - 28"},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437883","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}
Human Activity Recognition (HAR) based on embedded sensor data has become a popular research topic in ubiquitous computing, which has a wide range of practical applications in various fields such as human-computer interaction, healthcare, and motion tracking. Due to the difficulties of annotating sensing data, unsupervised and semi-supervised HAR methods are extensively studied, but their performance gap to the fully-supervised methods is notable. In this paper, we proposed a novel cross-modal co-learning approach called TS2ACT to achieve few-shot HAR. It introduces a cross-modal dataset augmentation method that uses the semantic-rich label text to search for human activity images to form an augmented dataset consisting of partially-labeled time series and fully-labeled images. Then it adopts a pre-trained CLIP image encoder to jointly train with a time series encoder using contrastive learning, where the time series and images are brought closer in feature space if they belong to the same activity class. For inference, the feature extracted from the input time series is compared with the embedding of a pre-trained CLIP text encoder using prompt learning, and the best match is output as the HAR classification results. We conducted extensive experiments on four public datasets to evaluate the performance of the proposed method. The numerical results show that TS2ACT significantly outperforms the state-of-the-art HAR methods, and it achieves performance close to or better than the fully supervised methods even using as few as 1% labeled data for model training. The source codes of TS2ACT are publicly available on GitHub1.
基于嵌入式传感器数据的人类活动识别（HAR）已成为泛在计算领域的热门研究课题，在人机交互、医疗保健和运动跟踪等多个领域有着广泛的实际应用。由于感知数据注释的困难，无监督和半监督 HAR 方法被广泛研究，但其性能与全监督方法相比差距明显。在本文中，我们提出了一种名为 TS2ACT 的新型跨模态协同学习方法，以实现少点 HAR。它引入了一种跨模态数据集增强方法，利用语义丰富的标签文本搜索人类活动图像，形成一个由部分标签时间序列和完全标签图像组成的增强数据集。然后，它采用对比学习方法，将预先训练好的 CLIP 图像编码器与时间序列编码器联合训练，如果时间序列和图像属于同一活动类别，则在特征空间中将它们拉近。在推理过程中，从输入时间序列中提取的特征会与预先训练好的 CLIP 文本编码器的嵌入进行比较，然后输出最佳匹配结果作为 HAR 分类结果。我们在四个公共数据集上进行了大量实验，以评估所提出方法的性能。数值结果表明，TS2ACT 的性能明显优于最先进的 HAR 方法，即使只使用 1% 的标注数据进行模型训练，它也能达到接近或优于完全监督方法的性能。TS2ACT 的源代码可在 GitHub 上公开获取1。
{"title":"TS2ACT","authors":"Kang Xia, Wenzhong Li, Shiwei Gan, Sanglu Lu","doi":"10.1145/3631445","DOIUrl":"https://doi.org/10.1145/3631445","url":null,"abstract":"Human Activity Recognition (HAR) based on embedded sensor data has become a popular research topic in ubiquitous computing, which has a wide range of practical applications in various fields such as human-computer interaction, healthcare, and motion tracking. Due to the difficulties of annotating sensing data, unsupervised and semi-supervised HAR methods are extensively studied, but their performance gap to the fully-supervised methods is notable. In this paper, we proposed a novel cross-modal co-learning approach called TS2ACT to achieve few-shot HAR. It introduces a cross-modal dataset augmentation method that uses the semantic-rich label text to search for human activity images to form an augmented dataset consisting of partially-labeled time series and fully-labeled images. Then it adopts a pre-trained CLIP image encoder to jointly train with a time series encoder using contrastive learning, where the time series and images are brought closer in feature space if they belong to the same activity class. For inference, the feature extracted from the input time series is compared with the embedding of a pre-trained CLIP text encoder using prompt learning, and the best match is output as the HAR classification results. We conducted extensive experiments on four public datasets to evaluate the performance of the proposed method. The numerical results show that TS2ACT significantly outperforms the state-of-the-art HAR methods, and it achieves performance close to or better than the fully supervised methods even using as few as 1% labeled data for model training. The source codes of TS2ACT are publicly available on GitHub1.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"1 10","pages":"1 - 22"},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437915","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}
Mayara Costa Figueiredo, Elizabeth A. Ankrah, Jacquelyn E. Powell, Daniel A. Epstein, Yunan Chen
Recently, there has been a proliferation of personal health applications describing to use Artificial Intelligence (AI) to assist health consumers in making health decisions based on their data and algorithmic outputs. However, it is still unclear how such descriptions influence individuals' perceptions of such apps and their recommendations. We therefore investigate how current AI descriptions influence individuals' attitudes towards algorithmic recommendations in fertility self-tracking through a simulated study using three versions of a fertility app. We found that participants preferred AI descriptions with explanation, which they perceived as more accurate and trustworthy. Nevertheless, they were unwilling to rely on these apps for high-stakes goals because of the potential consequences of a failure. We then discuss the importance of health goals for AI acceptance, how literacy and assumptions influence perceptions of AI descriptions and explanations, and the limitations of transparency in the context of algorithmic decision-making for personal health.
{"title":"Powered by AI","authors":"Mayara Costa Figueiredo, Elizabeth A. Ankrah, Jacquelyn E. Powell, Daniel A. Epstein, Yunan Chen","doi":"10.1145/3631414","DOIUrl":"https://doi.org/10.1145/3631414","url":null,"abstract":"Recently, there has been a proliferation of personal health applications describing to use Artificial Intelligence (AI) to assist health consumers in making health decisions based on their data and algorithmic outputs. However, it is still unclear how such descriptions influence individuals' perceptions of such apps and their recommendations. We therefore investigate how current AI descriptions influence individuals' attitudes towards algorithmic recommendations in fertility self-tracking through a simulated study using three versions of a fertility app. We found that participants preferred AI descriptions with explanation, which they perceived as more accurate and trustworthy. Nevertheless, they were unwilling to rely on these apps for high-stakes goals because of the potential consequences of a failure. We then discuss the importance of health goals for AI acceptance, how literacy and assumptions influence perceptions of AI descriptions and explanations, and the limitations of transparency in the context of algorithmic decision-making for personal health.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"9 11","pages":"1 - 24"},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437954","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}
Early screening for dry eye disease (DED) is crucial to identify and provide timely intervention to high-risk susceptible populations. Currently, clinical methods for diagnosing DED include the tear break-up time test, meibomian gland analysis, tear osmolarity test, and tear river height test, which require in-hospital detection. Unfortunately, there is no convenient way to screen for DED yet. In this paper, we propose SDE, a contactless, convenient, and ubiquitous DED screening system based on RF signals. To extract biomarkers for early screening of DED from RF signals, we construct frame chirps variance and extract fine-grained spontaneous blinking action. SDE is carefully designed to remove interference in RF signals and refine the characterization of biomarkers that denote the symptoms of DED. To endow SDE with the ability to adapt to new users, we develop a deep learning-based model of unsupervised domain adaptation to remove the influence of different users and environments in local and global two-level feature spaces. We conduct extensive experiments to evaluate SDE with 54 volunteers in 4 scenes. The experimental results confirm that SDE can accurately screen for DED in a new user in real environments such as eye examination rooms, clinics, offices, and homes.
{"title":"SDE","authors":"Meng Xue, Yuyang Zeng, Shengkang Gu, Qian Zhang, Bowei Tian, Changzheng Chen","doi":"10.1145/3631438","DOIUrl":"https://doi.org/10.1145/3631438","url":null,"abstract":"Early screening for dry eye disease (DED) is crucial to identify and provide timely intervention to high-risk susceptible populations. Currently, clinical methods for diagnosing DED include the tear break-up time test, meibomian gland analysis, tear osmolarity test, and tear river height test, which require in-hospital detection. Unfortunately, there is no convenient way to screen for DED yet. In this paper, we propose SDE, a contactless, convenient, and ubiquitous DED screening system based on RF signals. To extract biomarkers for early screening of DED from RF signals, we construct frame chirps variance and extract fine-grained spontaneous blinking action. SDE is carefully designed to remove interference in RF signals and refine the characterization of biomarkers that denote the symptoms of DED. To endow SDE with the ability to adapt to new users, we develop a deep learning-based model of unsupervised domain adaptation to remove the influence of different users and environments in local and global two-level feature spaces. We conduct extensive experiments to evaluate SDE with 54 volunteers in 4 scenes. The experimental results confirm that SDE can accurately screen for DED in a new user in real environments such as eye examination rooms, clinics, offices, and homes.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"1 2","pages":"1 - 23"},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437961","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}
Tracking the angular movement of body joints has been a critical enabler for various applications, such as virtual and augmented reality, sports monitoring, and medical rehabilitation. Despite the strong demand for accurate joint tracking, existing techniques, such as cameras, IMUs, and flex sensors, suffer from major limitations that include occlusion, cumulative error, and high cost. These issues collectively undermine the practicality of joint tracking. We introduce MagDot, a new magnetic-based joint tracking method that enables high-accuracy, drift-free, and wearable joint angle tracking. To overcome the limitations of existing techniques, MagDot employs a novel tracking scheme that compensates for various real-world impacts, achieving high tracking accuracy. We tested MagDot on eight participants with a professional motion capture system, i.e., Qualisys motion capture system with nine Arqus A12 cameras. The results indicate MagDot can accurately track major body joints. For example, MagDot can achieve tracking accuracy of 2.72°, 4.14°, and 4.61° for elbow, knee, and shoulder, respectively. With a power consumption of only 98 mW, MagDot can support one-day usage with a small battery pack.
{"title":"MagDot","authors":"Dongyao Chen, Qing Luo, Xiaomeng Chen, Xinbing Wang, Chenghui Zhou","doi":"10.1145/3631423","DOIUrl":"https://doi.org/10.1145/3631423","url":null,"abstract":"Tracking the angular movement of body joints has been a critical enabler for various applications, such as virtual and augmented reality, sports monitoring, and medical rehabilitation. Despite the strong demand for accurate joint tracking, existing techniques, such as cameras, IMUs, and flex sensors, suffer from major limitations that include occlusion, cumulative error, and high cost. These issues collectively undermine the practicality of joint tracking. We introduce MagDot, a new magnetic-based joint tracking method that enables high-accuracy, drift-free, and wearable joint angle tracking. To overcome the limitations of existing techniques, MagDot employs a novel tracking scheme that compensates for various real-world impacts, achieving high tracking accuracy. We tested MagDot on eight participants with a professional motion capture system, i.e., Qualisys motion capture system with nine Arqus A12 cameras. The results indicate MagDot can accurately track major body joints. For example, MagDot can achieve tracking accuracy of 2.72°, 4.14°, and 4.61° for elbow, knee, and shoulder, respectively. With a power consumption of only 98 mW, MagDot can support one-day usage with a small battery pack.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"8 8","pages":"1 - 25"},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139438005","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}
Qiushi Zhou, B. V. Syiem, Beier Li, Eduardo Velloso
We propose Reflected Reality: a new dimension for augmented reality that expands the augmented physical space into mirror reflections. By synchronously tracking the physical space in front of the mirror and the reflection behind it using an AR headset and an optional smart mirror component, reflected reality enables novel AR interactions that allow users to use their physical and reflected bodies to find and interact with virtual objects. We propose a design space for AR interaction with mirror reflections, and instantiate it using a prototype system featuring a HoloLens 2 and a smart mirror. We explore the design space along the following dimensions: the user's perspective of input, the spatial frame of reference, and the direction of the mirror space relative to the physical space. Using our prototype, we visualise a use case scenario that traverses the design space to demonstrate its interaction affordances in a practical context. To understand how users perceive the intuitiveness and ease of reflected reality interaction, we conducted an exploratory and a formal user evaluation studies to characterise user performance of AR interaction tasks in reflected reality. We discuss the unique interaction affordances that reflected reality offers, and outline possibilities of its future applications.
我们提出了 "反射现实"（Reflected Reality）：增强现实的一个新维度，它将增强物理空间扩展到镜面反射中。通过使用 AR 头显和可选的智能镜子组件同步跟踪镜子前的物理空间和镜子后的反射，反射现实可以实现新颖的 AR 互动，让用户可以使用他们的物理和反射身体来找到虚拟对象并与之互动。我们提出了利用镜面反射进行 AR 互动的设计空间，并利用 HoloLens 2 和智能镜子的原型系统将其实例化。我们沿着以下维度探索设计空间：用户的输入视角、空间参照系以及镜像空间相对于物理空间的方向。利用我们的原型，我们可视化了一个穿越设计空间的用例场景，以展示其在实际环境中的交互能力。为了了解用户如何感知反射现实交互的直观性和易用性，我们进行了一项探索性和正式的用户评估研究，以描述用户在反射现实中执行 AR 交互任务的表现。我们讨论了反射现实所提供的独特交互能力，并概述了其未来应用的可能性。
{"title":"Reflected Reality","authors":"Qiushi Zhou, B. V. Syiem, Beier Li, Eduardo Velloso","doi":"10.1145/3631431","DOIUrl":"https://doi.org/10.1145/3631431","url":null,"abstract":"We propose Reflected Reality: a new dimension for augmented reality that expands the augmented physical space into mirror reflections. By synchronously tracking the physical space in front of the mirror and the reflection behind it using an AR headset and an optional smart mirror component, reflected reality enables novel AR interactions that allow users to use their physical and reflected bodies to find and interact with virtual objects. We propose a design space for AR interaction with mirror reflections, and instantiate it using a prototype system featuring a HoloLens 2 and a smart mirror. We explore the design space along the following dimensions: the user's perspective of input, the spatial frame of reference, and the direction of the mirror space relative to the physical space. Using our prototype, we visualise a use case scenario that traverses the design space to demonstrate its interaction affordances in a practical context. To understand how users perceive the intuitiveness and ease of reflected reality interaction, we conducted an exploratory and a formal user evaluation studies to characterise user performance of AR interaction tasks in reflected reality. We discuss the unique interaction affordances that reflected reality offers, and outline possibilities of its future applications.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"2 4","pages":"1 - 28"},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139438023","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}
We present PyroSense, the first-of-its-kind system that enables fine-grained 3D posture reconstruction using ubiquitous COTS passive infrared sensor (PIR sensor). PyroSense senses heat signals generated by the human body and airflow due to body movement to reconstruct the corresponding human postures in real time. PyroSense greatly advances the prior PIR-based sensing design by improving the sensitivity of COTS PIR sensor to body movement, increasing spatial resolution without additional deployment overhead, and designing intellectual algorithms to adapt to diverse environmental factors. We build a low-cost PyroSense prototype using off-the-shelf hardware components. The experimental findings indicate that PyroSense not only attains a classification accuracy of 99.46% across 15 classes, but it also registers a mean joint distance error of less than 16 cm for 14 body joints for posture reconstruction in challenging environments.
{"title":"PyroSense","authors":"Huaili Zeng, Gen Li, Tianxing Li","doi":"10.1145/3631435","DOIUrl":"https://doi.org/10.1145/3631435","url":null,"abstract":"We present PyroSense, the first-of-its-kind system that enables fine-grained 3D posture reconstruction using ubiquitous COTS passive infrared sensor (PIR sensor). PyroSense senses heat signals generated by the human body and airflow due to body movement to reconstruct the corresponding human postures in real time. PyroSense greatly advances the prior PIR-based sensing design by improving the sensitivity of COTS PIR sensor to body movement, increasing spatial resolution without additional deployment overhead, and designing intellectual algorithms to adapt to diverse environmental factors. We build a low-cost PyroSense prototype using off-the-shelf hardware components. The experimental findings indicate that PyroSense not only attains a classification accuracy of 99.46% across 15 classes, but it also registers a mean joint distance error of less than 16 cm for 14 body joints for posture reconstruction in challenging environments.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"13 5","pages":"1 - 32"},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437378","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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