Liqiong Chang, Xiaofeng Yang, Ruyue Liu, Guodong Xie, Fuwei Wang, Ju Wang
Material sensing is crucial in many emerging applications, such as waste classification and hazardous material detection. Although existing Radio Frequency (RF) signal based systems achieved great success, they have limited identification accuracy when either RF signals can not penetrate through a target or a target has different outer and inner materials. This paper introduces a Frequency Selective Surface (FSS) tag based high accuracy material identification system, namely FSS-Tag, which utilises both the penetrating signals and the coupling effect. Specifically, we design and attach a FSS tag to a target, and use frequency responses of the tag for material sensing, since different target materials have different frequency responses. The key advantage of our system is that, when RF signals pass through a target with the FSS tag, the penetrating signal responds more to the inner material, and the coupling effect (between the target and the tag) reflects more about the outer material; thus, one can achieve a higher sensing accuracy. The challenge lies in how to find optimal tag design parameters so that the frequency response of different target materials can be clearly distinguished. We address this challenge by establishing a tag parameter optimization model. Real-world experiments show that FSS-Tag achieves more than 91% accuracy on identifying eight common materials, and improves the accuracy by up to 38% and 8% compared with the state of the art (SOTA) penetrating signal based method TagScan and the SOTA coupling effect based method Tagtag.
{"title":"FSS-Tag","authors":"Liqiong Chang, Xiaofeng Yang, Ruyue Liu, Guodong Xie, Fuwei Wang, Ju Wang","doi":"10.1145/3631457","DOIUrl":"https://doi.org/10.1145/3631457","url":null,"abstract":"Material sensing is crucial in many emerging applications, such as waste classification and hazardous material detection. Although existing Radio Frequency (RF) signal based systems achieved great success, they have limited identification accuracy when either RF signals can not penetrate through a target or a target has different outer and inner materials. This paper introduces a Frequency Selective Surface (FSS) tag based high accuracy material identification system, namely FSS-Tag, which utilises both the penetrating signals and the coupling effect. Specifically, we design and attach a FSS tag to a target, and use frequency responses of the tag for material sensing, since different target materials have different frequency responses. The key advantage of our system is that, when RF signals pass through a target with the FSS tag, the penetrating signal responds more to the inner material, and the coupling effect (between the target and the tag) reflects more about the outer material; thus, one can achieve a higher sensing accuracy. The challenge lies in how to find optimal tag design parameters so that the frequency response of different target materials can be clearly distinguished. We address this challenge by establishing a tag parameter optimization model. Real-world experiments show that FSS-Tag achieves more than 91% accuracy on identifying eight common materials, and improves the accuracy by up to 38% and 8% compared with the state of the art (SOTA) penetrating signal based method TagScan and the SOTA coupling effect based method Tagtag.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"10 42","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":"139437934","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}
Yan Liu, Anlan Yu, Leye Wang, Bin Guo, Yang Li, E. Yi, Daqing Zhang
In recent years, considerable endeavors have been devoted to exploring Wi-Fi-based sensing technologies by modeling the intricate mapping between received signals and corresponding human activities. However, the inherent complexity of Wi-Fi signals poses significant challenges for practical applications due to their pronounced susceptibility to deployment environments. To address this challenge, we delve into the distinctive characteristics of Wi-Fi signals and distill three pivotal factors that can be leveraged to enhance generalization capabilities of deep learning-based Wi-Fi sensing models: 1) effectively capture valuable input to mitigate the adverse impact of noisy measurements; 2) adaptively fuse complementary information from multiple Wi-Fi devices to boost the distinguishability of signal patterns associated with different activities; 3) extract generalizable features that can overcome the inconsistent representations of activities under different environmental conditions (e.g., locations, orientations). Leveraging these insights, we design a novel and unified sensing framework based on Wi-Fi signals, dubbed UniFi, and use gesture recognition as an application to demonstrate its effectiveness. UniFi achieves robust and generalizable gesture recognition in real-world scenarios by extracting discriminative and consistent features unrelated to environmental factors from pre-denoised signals collected by multiple transceivers. To achieve this, we first introduce an effective signal preprocessing approach that captures the applicable input data from noisy received signals for the deep learning model. Second, we propose a multi-view deep network based on spatio-temporal cross-view attention that integrates multi-carrier and multi-device signals to extract distinguishable information. Finally, we present the mutual information maximization as a regularizer to learn environment-invariant representations via contrastive loss without requiring access to any signals from unseen environments for practical adaptation. Extensive experiments on the Widar 3.0 dataset demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in different settings (99% and 90%-98% accuracy for in-domain and cross-domain recognition without additional data collection and model training).
{"title":"UniFi","authors":"Yan Liu, Anlan Yu, Leye Wang, Bin Guo, Yang Li, E. Yi, Daqing Zhang","doi":"10.1145/3631429","DOIUrl":"https://doi.org/10.1145/3631429","url":null,"abstract":"In recent years, considerable endeavors have been devoted to exploring Wi-Fi-based sensing technologies by modeling the intricate mapping between received signals and corresponding human activities. However, the inherent complexity of Wi-Fi signals poses significant challenges for practical applications due to their pronounced susceptibility to deployment environments. To address this challenge, we delve into the distinctive characteristics of Wi-Fi signals and distill three pivotal factors that can be leveraged to enhance generalization capabilities of deep learning-based Wi-Fi sensing models: 1) effectively capture valuable input to mitigate the adverse impact of noisy measurements; 2) adaptively fuse complementary information from multiple Wi-Fi devices to boost the distinguishability of signal patterns associated with different activities; 3) extract generalizable features that can overcome the inconsistent representations of activities under different environmental conditions (e.g., locations, orientations). Leveraging these insights, we design a novel and unified sensing framework based on Wi-Fi signals, dubbed UniFi, and use gesture recognition as an application to demonstrate its effectiveness. UniFi achieves robust and generalizable gesture recognition in real-world scenarios by extracting discriminative and consistent features unrelated to environmental factors from pre-denoised signals collected by multiple transceivers. To achieve this, we first introduce an effective signal preprocessing approach that captures the applicable input data from noisy received signals for the deep learning model. Second, we propose a multi-view deep network based on spatio-temporal cross-view attention that integrates multi-carrier and multi-device signals to extract distinguishable information. Finally, we present the mutual information maximization as a regularizer to learn environment-invariant representations via contrastive loss without requiring access to any signals from unseen environments for practical adaptation. Extensive experiments on the Widar 3.0 dataset demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in different settings (99% and 90%-98% accuracy for in-domain and cross-domain recognition without additional data collection and model training).","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"14 8","pages":"1 - 29"},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437316","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}
Marvin Martin, Etienne Meunier, P. Moreau, Jean-Eudes Gadenne, J. Dautel, Félicien Catherin, Eugene Pinsky, Reza Rawassizadeh
Due to global warming, sharks are moving closer to the beaches, affecting the risk to humans and their own lives. Within the past decade, several technologies were developed to reduce the risks for swimmers and surfers. This study proposes a robust method based on computer vision to detect sharks using an underwater camera monitoring system to secure coastlines. The system is autonomous, environment-friendly, and requires low maintenance. 43,679 images extracted from 175 hours of videos of marine life were used to train our algorithms. Our approach allows the collection and analysis of videos in real-time using an autonomous underwater camera connected to a smart buoy charged with solar panels. The videos are processed by a Domain Adversarial Convolutional Neural Network to discern sharks regardless of the background environment with an F2-score of 83.2% and a recall of 90.9%, while human experts have an F2-score of 94% and a recall of 95.7%.
{"title":"ADA-SHARK","authors":"Marvin Martin, Etienne Meunier, P. Moreau, Jean-Eudes Gadenne, J. Dautel, Félicien Catherin, Eugene Pinsky, Reza Rawassizadeh","doi":"10.1145/3631416","DOIUrl":"https://doi.org/10.1145/3631416","url":null,"abstract":"Due to global warming, sharks are moving closer to the beaches, affecting the risk to humans and their own lives. Within the past decade, several technologies were developed to reduce the risks for swimmers and surfers. This study proposes a robust method based on computer vision to detect sharks using an underwater camera monitoring system to secure coastlines. The system is autonomous, environment-friendly, and requires low maintenance. 43,679 images extracted from 175 hours of videos of marine life were used to train our algorithms. Our approach allows the collection and analysis of videos in real-time using an autonomous underwater camera connected to a smart buoy charged with solar panels. The videos are processed by a Domain Adversarial Convolutional Neural Network to discern sharks regardless of the background environment with an F2-score of 83.2% and a recall of 90.9%, while human experts have an F2-score of 94% and a recall of 95.7%.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"12 2","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":"139437744","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}
Wearable sensor-based human activity recognition (HAR) has gained significant attention due to the widespread use of smart wearable devices. However, variations in different subjects can cause a domain shift that impedes the scaling of the recognition model. Unsupervised domain adaptation has been proposed as a solution to recognize activities in new, unlabeled target domains by training the source and target data together. However, the need for accessing source data raises privacy concerns. Source-free domain adaptation has emerged as a practical setting, where only a pre-trained source model is provided for the unlabeled target domain. This setup aligns with the need for personalized activity model adaptation on target local devices. As the edge devices are resource-constrained with limited memory, it is crucial to take the computational efficiency, i.e., memory cost into consideration. In this paper, we develop a source-free domain adaptation framework for wearable sensor-based HAR, with a focus on computational efficiency for target edge devices. Firstly, we design a lightweight add-on module called adapter to adapt the frozen pre-trained model to the unlabeled target domain. Secondly, to optimize the adapter, we adopt a simple yet effective model adaptation method that leverages local representation similarity and prediction consistency. Additionally, we design a set of sample selection optimization strategies to select samples effective for adaptation and further enhance computational efficiency while maintaining adaptation performance. Our extensive experiments on three datasets demonstrate that our method achieves comparable recognition accuracy to the state-of-the-art source free domain adaptation methods with fewer than 1% of the parameters updated and saves up to 4.99X memory cost.
由于智能可穿戴设备的广泛使用，基于可穿戴传感器的人类活动识别（HAR）受到了广泛关注。然而，不同主体的变化会导致领域转移，从而阻碍识别模型的扩展。有人提出了一种无监督领域适应解决方案，通过将源数据和目标数据一起训练，在新的、无标记的目标领域中识别活动。然而，访问源数据的需要会引发隐私问题。无源域适配已成为一种实用的设置，在这种设置中，只为未标记的目标域提供预先训练好的源模型。这种设置符合在目标本地设备上进行个性化活动模型适配的需求。由于边缘设备资源有限，内存有限，因此必须考虑计算效率，即内存成本。在本文中，我们为基于传感器的可穿戴 HAR 开发了一个无源域适配框架，重点关注目标边缘设备的计算效率。首先，我们设计了一个名为适配器的轻量级附加模块，用于将冻结的预训练模型适配到未标记的目标领域。其次，为了优化适配器，我们采用了一种简单而有效的模型适配方法，该方法利用了局部表示相似性和预测一致性。此外，我们还设计了一套样本选择优化策略，以选择对适配有效的样本，并在保持适配性能的同时进一步提高计算效率。我们在三个数据集上进行的大量实验证明，我们的方法只需更新不到 1% 的参数，就能达到与最先进的无源域适配方法相当的识别准确率，并节省高达 4.99 倍的内存成本。
{"title":"SF-Adapter","authors":"Hua Kang, Qingyong Hu, Qian Zhang","doi":"10.1145/3631428","DOIUrl":"https://doi.org/10.1145/3631428","url":null,"abstract":"Wearable sensor-based human activity recognition (HAR) has gained significant attention due to the widespread use of smart wearable devices. However, variations in different subjects can cause a domain shift that impedes the scaling of the recognition model. Unsupervised domain adaptation has been proposed as a solution to recognize activities in new, unlabeled target domains by training the source and target data together. However, the need for accessing source data raises privacy concerns. Source-free domain adaptation has emerged as a practical setting, where only a pre-trained source model is provided for the unlabeled target domain. This setup aligns with the need for personalized activity model adaptation on target local devices. As the edge devices are resource-constrained with limited memory, it is crucial to take the computational efficiency, i.e., memory cost into consideration. In this paper, we develop a source-free domain adaptation framework for wearable sensor-based HAR, with a focus on computational efficiency for target edge devices. Firstly, we design a lightweight add-on module called adapter to adapt the frozen pre-trained model to the unlabeled target domain. Secondly, to optimize the adapter, we adopt a simple yet effective model adaptation method that leverages local representation similarity and prediction consistency. Additionally, we design a set of sample selection optimization strategies to select samples effective for adaptation and further enhance computational efficiency while maintaining adaptation performance. Our extensive experiments on three datasets demonstrate that our method achieves comparable recognition accuracy to the state-of-the-art source free domain adaptation methods with fewer than 1% of the parameters updated and saves up to 4.99X memory cost.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"1 4","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":"139437846","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 propose a novel sensorless approach to indoor localization by leveraging natural language conversations with users, which we call conversational localization. To show the feasibility of conversational localization, we develop a proof-of-concept system that guides users to describe their surroundings in a chat and estimates their position based on the information they provide. We devised a modular architecture for our system with four modules. First, we construct an entity database with available image-based floor maps. Second, we enable the dynamic identification and scoring of information provided by users through our utterance processing module. Then, we implement a conversational agent that can intelligently strategize and guide the interaction to elicit localizationally valuable information from users. Finally, we employ visibility catchment area and line-of-sight heuristics to generate spatial estimates for the user's location. We conduct two user studies in designing and testing the system. We collect 800 natural language descriptions of unfamiliar indoor spaces in an online crowdsourcing study to learn the feasibility of extracting localizationally useful entities from user utterances. We then conduct a field study with 10 participants at 10 locations to evaluate the feasibility and performance of conversational localization. The results show that conversational localization can achieve within-10 meter localization accuracy at eight out of the ten study sites, showing the technique's utility for classes of indoor location-based services.
{"title":"Conversational Localization","authors":"Smitha Sheshadri, Kotaro Hara","doi":"10.1145/3631404","DOIUrl":"https://doi.org/10.1145/3631404","url":null,"abstract":"We propose a novel sensorless approach to indoor localization by leveraging natural language conversations with users, which we call conversational localization. To show the feasibility of conversational localization, we develop a proof-of-concept system that guides users to describe their surroundings in a chat and estimates their position based on the information they provide. We devised a modular architecture for our system with four modules. First, we construct an entity database with available image-based floor maps. Second, we enable the dynamic identification and scoring of information provided by users through our utterance processing module. Then, we implement a conversational agent that can intelligently strategize and guide the interaction to elicit localizationally valuable information from users. Finally, we employ visibility catchment area and line-of-sight heuristics to generate spatial estimates for the user's location. We conduct two user studies in designing and testing the system. We collect 800 natural language descriptions of unfamiliar indoor spaces in an online crowdsourcing study to learn the feasibility of extracting localizationally useful entities from user utterances. We then conduct a field study with 10 participants at 10 locations to evaluate the feasibility and performance of conversational localization. The results show that conversational localization can achieve within-10 meter localization accuracy at eight out of the ten study sites, showing the technique's utility for classes of indoor location-based services.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"11 7","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":"139437869","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}
Ke He, Chentao Li, Yongjie Duan, Jianjiang Feng, Jie Zhou
Several studies have explored the estimation of finger pose/angle to enhance the expressiveness of touchscreens. However, the accuracy of previous algorithms is limited by large estimation errors, and the sequential output angles are unstable, making it difficult to meet the demands of practical applications. We believe the defect arises from improper rotation representation, the lack of time-series modeling, and the difficulty in accommodating individual differences among users. To address these issues, we conduct in-depth study of rotation representation for the 2D pose problem by minimizing the errors between representation space and original space. A deep learning model, TrackPose, using a self-attention mechanism is proposed for time-series modeling to improve accuracy and stability of finger pose. A registration application on a mobile phone is developed to collect touchscreen images of each new user without the use of optical tracking device. The combination of the three measures mentioned above has resulted in a 33% reduction in the angle estimation error, 47% for the yaw angle especially. Additionally, the instability of sequential estimations, measured by the proposed metric MAEΔ, is reduced by 62%. User study further confirms the effectiveness of our proposed algorithm.
{"title":"TrackPose","authors":"Ke He, Chentao Li, Yongjie Duan, Jianjiang Feng, Jie Zhou","doi":"10.1145/3631459","DOIUrl":"https://doi.org/10.1145/3631459","url":null,"abstract":"Several studies have explored the estimation of finger pose/angle to enhance the expressiveness of touchscreens. However, the accuracy of previous algorithms is limited by large estimation errors, and the sequential output angles are unstable, making it difficult to meet the demands of practical applications. We believe the defect arises from improper rotation representation, the lack of time-series modeling, and the difficulty in accommodating individual differences among users. To address these issues, we conduct in-depth study of rotation representation for the 2D pose problem by minimizing the errors between representation space and original space. A deep learning model, TrackPose, using a self-attention mechanism is proposed for time-series modeling to improve accuracy and stability of finger pose. A registration application on a mobile phone is developed to collect touchscreen images of each new user without the use of optical tracking device. The combination of the three measures mentioned above has resulted in a 33% reduction in the angle estimation error, 47% for the yaw angle especially. Additionally, the instability of sequential estimations, measured by the proposed metric MAEΔ, is reduced by 62%. User study further confirms the effectiveness of our proposed algorithm.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"11 7","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":"139437891","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}
Xusheng Zhang, Duo Zhang, Yaxiong Xie, Dan Wu, Yang Li, Daqing Zhang
The bathroom has consistently ranked among the most perilous rooms in households, with slip and fall incidents during showers posing a critical threat, particularly to the elders. To address this concern while ensuring privacy and accuracy, the mmWave-based sensing system has emerged as a promising solution. Capable of precisely detecting human activities and promptly triggering alarms in response to critical events, it has proved especially valuable within bathroom environments. However, deploying such a system in bathrooms faces a significant challenge: interference from running water. Similar to the human body, water droplets reflect substantial mmWave signals, presenting a major obstacle to accurate sensing. Through rigorous empirical study, we confirm that the interference caused by running water adheres to a Weibull distribution, offering insight into its behavior. Leveraging this understanding, we propose a customized Constant False Alarm Rate (CFAR) detector, specifically tailored to handle the interference from running water. This innovative detector effectively isolates human-generated signals, thus enabling accurate human detection even in the presence of running water interference. Our implementation of "Waffle" on a commercial off-the-shelf mmWave radar demonstrates exceptional sensing performance. It achieves median errors of 1.8cm and 6.9cm for human height estimation and tracking, respectively, even in the presence of running water. Furthermore, our fall detection system, built upon this technique, achieves remarkable performance (a recall of 97.2% and an accuracy of 97.8%), surpassing the state-of-the-art method.
{"title":"Waffle","authors":"Xusheng Zhang, Duo Zhang, Yaxiong Xie, Dan Wu, Yang Li, Daqing Zhang","doi":"10.1145/3631458","DOIUrl":"https://doi.org/10.1145/3631458","url":null,"abstract":"The bathroom has consistently ranked among the most perilous rooms in households, with slip and fall incidents during showers posing a critical threat, particularly to the elders. To address this concern while ensuring privacy and accuracy, the mmWave-based sensing system has emerged as a promising solution. Capable of precisely detecting human activities and promptly triggering alarms in response to critical events, it has proved especially valuable within bathroom environments. However, deploying such a system in bathrooms faces a significant challenge: interference from running water. Similar to the human body, water droplets reflect substantial mmWave signals, presenting a major obstacle to accurate sensing. Through rigorous empirical study, we confirm that the interference caused by running water adheres to a Weibull distribution, offering insight into its behavior. Leveraging this understanding, we propose a customized Constant False Alarm Rate (CFAR) detector, specifically tailored to handle the interference from running water. This innovative detector effectively isolates human-generated signals, thus enabling accurate human detection even in the presence of running water interference. Our implementation of \"Waffle\" on a commercial off-the-shelf mmWave radar demonstrates exceptional sensing performance. It achieves median errors of 1.8cm and 6.9cm for human height estimation and tracking, respectively, even in the presence of running water. Furthermore, our fall detection system, built upon this technique, achieves remarkable performance (a recall of 97.2% and an accuracy of 97.8%), surpassing the state-of-the-art method.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"10 39","pages":"1 - 29"},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437936","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}
Wasifur Rahman, Abdelrahman Abdelkader, Sangwu Lee, Phillip T. Yang, Md Saiful Islam, Tariq Adnan, Masum Hasan, Ellen Wagner, Sooyong Park, E. R. Dorsey, Catherine Schwartz, Karen Jaffe, Ehsan Hoque
We present a user-centric validation of a teleneurology platform, assessing its effectiveness in conveying screening information, facilitating user queries, and offering resources to enhance user empowerment. This validation process is implemented in the setting of Parkinson's disease (PD), in collaboration with a neurology department of a major medical center in the USA. Our intention is that with this platform, anyone globally with a webcam and microphone-equipped computer can carry out a series of speech, motor, and facial mimicry tasks. Our validation method demonstrates to users a mock PD risk assessment and provides access to relevant resources, including a chatbot driven by GPT, locations of local neurologists, and actionable and scientifically-backed PD prevention and management recommendations. We share findings from 91 participants (48 with PD, 43 without) aimed at evaluating the user experience and collecting feedback. Our framework was rated positively by 80.85% (standard deviation ± 8.92%) of the participants, and it achieved an above-average 70.42 (standard deviation ± 13.85) System-Usability-Scale (SUS) score. We also conducted a thematic analysis of open-ended feedback to further inform our future work. When given the option to ask any questions to the chatbot, participants typically asked for information about neurologists, screening results, and the community support group. We also provide a roadmap of how the knowledge generated in this paper can be generalized to screening frameworks for other diseases through designing appropriate recording environments, appropriate tasks, and tailored user-interfaces.
{"title":"A User-Centered Framework to Empower People with Parkinson's Disease","authors":"Wasifur Rahman, Abdelrahman Abdelkader, Sangwu Lee, Phillip T. Yang, Md Saiful Islam, Tariq Adnan, Masum Hasan, Ellen Wagner, Sooyong Park, E. R. Dorsey, Catherine Schwartz, Karen Jaffe, Ehsan Hoque","doi":"10.1145/3631430","DOIUrl":"https://doi.org/10.1145/3631430","url":null,"abstract":"We present a user-centric validation of a teleneurology platform, assessing its effectiveness in conveying screening information, facilitating user queries, and offering resources to enhance user empowerment. This validation process is implemented in the setting of Parkinson's disease (PD), in collaboration with a neurology department of a major medical center in the USA. Our intention is that with this platform, anyone globally with a webcam and microphone-equipped computer can carry out a series of speech, motor, and facial mimicry tasks. Our validation method demonstrates to users a mock PD risk assessment and provides access to relevant resources, including a chatbot driven by GPT, locations of local neurologists, and actionable and scientifically-backed PD prevention and management recommendations. We share findings from 91 participants (48 with PD, 43 without) aimed at evaluating the user experience and collecting feedback. Our framework was rated positively by 80.85% (standard deviation ± 8.92%) of the participants, and it achieved an above-average 70.42 (standard deviation ± 13.85) System-Usability-Scale (SUS) score. We also conducted a thematic analysis of open-ended feedback to further inform our future work. When given the option to ask any questions to the chatbot, participants typically asked for information about neurologists, screening results, and the community support group. We also provide a roadmap of how the knowledge generated in this paper can be generalized to screening frameworks for other diseases through designing appropriate recording environments, appropriate tasks, and tailored user-interfaces.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"1 1","pages":"1 - 29"},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437962","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 medical score calculator apps are widely used among practitioners to help make decisions regarding patient treatment and diagnosis. Errors in score definition, input, or calculations can result in severe and potentially life-threatening situations. Despite these high stakes, there has been no systematic or rigorous effort to examine and verify score calculator apps. We address these issues via a novel, interval-based score checking approach. Based on our observation that medical reference tables themselves may contain errors (which can propagate to apps) we first introduce automated correctness checking of reference tables. Specifically, we reduce score correctness checking to partition checking (coverage and non-overlap) over score parameters' ranges. We checked 12 scoring systems used in emergency, intensive, and acute care. Surprisingly, though some of these scores have been used for decades, we found errors in 5 score specifications: 8 coverage violations and 3 non-overlap violations. Second, we design and implement an automatic, dynamic analysis-based approach for verifying score correctness in a given Android app; the approach combines efficient, automatic GUI extraction and app exploration with partition/consistency checking to expose app errors. We applied the approach to 90 Android apps that implement medical score calculators. We found 23 coverage violations in 11 apps; 32 non-overlap violations in 12 apps, and 16 incorrect score calculations in 16 apps. We reported all findings to developers, which so far has led to fixes in 6 apps.
{"title":"Diagnosing Medical Score Calculator Apps","authors":"Sydur Rahaman, Raina Samuel, Iulian Neamtiu","doi":"10.1145/3610912","DOIUrl":"https://doi.org/10.1145/3610912","url":null,"abstract":"Mobile medical score calculator apps are widely used among practitioners to help make decisions regarding patient treatment and diagnosis. Errors in score definition, input, or calculations can result in severe and potentially life-threatening situations. Despite these high stakes, there has been no systematic or rigorous effort to examine and verify score calculator apps. We address these issues via a novel, interval-based score checking approach. Based on our observation that medical reference tables themselves may contain errors (which can propagate to apps) we first introduce automated correctness checking of reference tables. Specifically, we reduce score correctness checking to partition checking (coverage and non-overlap) over score parameters' ranges. We checked 12 scoring systems used in emergency, intensive, and acute care. Surprisingly, though some of these scores have been used for decades, we found errors in 5 score specifications: 8 coverage violations and 3 non-overlap violations. Second, we design and implement an automatic, dynamic analysis-based approach for verifying score correctness in a given Android app; the approach combines efficient, automatic GUI extraction and app exploration with partition/consistency checking to expose app errors. We applied the approach to 90 Android apps that implement medical score calculators. We found 23 coverage violations in 11 apps; 32 non-overlap violations in 12 apps, and 16 incorrect score calculations in 16 apps. We reported all findings to developers, which so far has led to fixes in 6 apps.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135535241","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}
Intelligent audio systems are ubiquitous in our lives, such as speech command recognition and speaker recognition. However, it is shown that deep learning-based intelligent audio systems are vulnerable to adversarial attacks. In this paper, we propose a physical adversarial attack that exploits reverberation, a natural indoor acoustic effect, to realize imperceptible, fast, and targeted black-box attacks. Unlike existing attacks that constrain the magnitude of adversarial perturbations within a fixed radius, we generate reverberation-alike perturbations that blend naturally with the original voice sample 1. Additionally, we can generate more robust adversarial examples even under over-the-air propagation by considering distortions in the physical environment. Extensive experiments are conducted using two popular intelligent audio systems in various situations, such as different room sizes, distance, and ambient noises. The results show that Echo can invade into intelligent audio systems in both digital and physical over-the-air environment.
{"title":"Echo","authors":"Meng Xue, Kuang Peng, Xueluan Gong, Qian Zhang, Yanjiao Chen, Routing Li","doi":"10.1145/3610874","DOIUrl":"https://doi.org/10.1145/3610874","url":null,"abstract":"Intelligent audio systems are ubiquitous in our lives, such as speech command recognition and speaker recognition. However, it is shown that deep learning-based intelligent audio systems are vulnerable to adversarial attacks. In this paper, we propose a physical adversarial attack that exploits reverberation, a natural indoor acoustic effect, to realize imperceptible, fast, and targeted black-box attacks. Unlike existing attacks that constrain the magnitude of adversarial perturbations within a fixed radius, we generate reverberation-alike perturbations that blend naturally with the original voice sample 1. Additionally, we can generate more robust adversarial examples even under over-the-air propagation by considering distortions in the physical environment. Extensive experiments are conducted using two popular intelligent audio systems in various situations, such as different room sizes, distance, and ambient noises. The results show that Echo can invade into intelligent audio systems in both digital and physical over-the-air environment.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135535369","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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