There are many scenarios where the liquid is occluded by other items ( e.g
在许多情况下，液体被其他物品(例如液体)堵塞
{"title":"PackquID: In-packet Liquid Identification Using RF Signals","authors":"Fei Shang, Panlong Yang, Yubo Yan, Xiangyang Li","doi":"10.1145/3569469","DOIUrl":"https://doi.org/10.1145/3569469","url":null,"abstract":"There are many scenarios where the liquid is occluded by other items ( e.g","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"21 1","pages":"181:1-181:27"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73214010","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}
T. Drey, Jessica Janek, Josef Lang, Dietmar Puschmann, Michael Rietzler, E. Rukzio
Educational apps support learning, but handwriting training is still based on analog pen- and paper. However, training handwriting with apps can negatively affect graphomotor handwriting skills due to the different haptic feedback of the tablet, stylus, or finger compared to pen and paper. With SpARklingPaper, we are the first to combine the genuine haptic feedback of analog pen and paper with the digital support of apps. Our artifact contribution enables children to write with any pen on a standard paper placed on a tablet’s screen, augmenting the paper from below, showing animated letters and individual feedback. We conducted two online surveys with overall 29 parents and teachers of elementary school pupils and a user study with 13 children and 13 parents for evaluation. Our results show the importance of the genuine analog haptic feedback combined with the augmentation of SpARklingPaper. It was rated superior compared to our stylus baseline condition regarding pen-handling, writing training-success, motivation, and overall impression. SpARklingPaper can be a blueprint for high-fidelity haptic feedback handwriting training systems.
{"title":"SpARklingPaper: Enhancing Common Pen- And Paper-Based Handwriting Training for Children by Digitally Augmenting Papers Using a Tablet Screen","authors":"T. Drey, Jessica Janek, Josef Lang, Dietmar Puschmann, Michael Rietzler, E. Rukzio","doi":"10.1145/3550337","DOIUrl":"https://doi.org/10.1145/3550337","url":null,"abstract":"Educational apps support learning, but handwriting training is still based on analog pen- and paper. However, training handwriting with apps can negatively affect graphomotor handwriting skills due to the different haptic feedback of the tablet, stylus, or finger compared to pen and paper. With SpARklingPaper, we are the first to combine the genuine haptic feedback of analog pen and paper with the digital support of apps. Our artifact contribution enables children to write with any pen on a standard paper placed on a tablet’s screen, augmenting the paper from below, showing animated letters and individual feedback. We conducted two online surveys with overall 29 parents and teachers of elementary school pupils and a user study with 13 children and 13 parents for evaluation. Our results show the importance of the genuine analog haptic feedback combined with the augmentation of SpARklingPaper. It was rated superior compared to our stylus baseline condition regarding pen-handling, writing training-success, motivation, and overall impression. SpARklingPaper can be a blueprint for high-fidelity haptic feedback handwriting training systems.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"102 1","pages":"113:1-113:29"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73864905","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 sensor-based recognition of Activities of Daily Living (ADLs) in smart-home environments is an active research area, with relevant applications in healthcare and ambient assisted living. The application of Explainable Artificial Intelligence (XAI) to ADLs recognition has the potential of making this process trusted, transparent and understandable. The few works that investigated this problem considered only interpretable machine learning models. In this work, we propose DeXAR, a novel methodology to transform sensor data into semantic images to take advantage of XAI methods based on Convolutional Neural Networks (CNN). We apply different XAI approaches for deep learning and, from the resulting heat maps, we generate explanations in natural language. In order to identify the most effective XAI method, we performed extensive experiments on two different datasets, with both a common-knowledge and a user-based evaluation. The results of a user study show that the white-box XAI method based on prototypes is the most effective.
{"title":"DeXAR: Deep Explainable Sensor-Based Activity Recognition in Smart-Home Environments","authors":"Luca Arrotta, Gabriele Civitarese, C. Bettini","doi":"10.1145/3517224","DOIUrl":"https://doi.org/10.1145/3517224","url":null,"abstract":"The sensor-based recognition of Activities of Daily Living (ADLs) in smart-home environments is an active research area, with relevant applications in healthcare and ambient assisted living. The application of Explainable Artificial Intelligence (XAI) to ADLs recognition has the potential of making this process trusted, transparent and understandable. The few works that investigated this problem considered only interpretable machine learning models. In this work, we propose DeXAR, a novel methodology to transform sensor data into semantic images to take advantage of XAI methods based on Convolutional Neural Networks (CNN). We apply different XAI approaches for deep learning and, from the resulting heat maps, we generate explanations in natural language. In order to identify the most effective XAI method, we performed extensive experiments on two different datasets, with both a common-knowledge and a user-based evaluation. The results of a user study show that the white-box XAI method based on prototypes is the most effective.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"14 1","pages":"1:1-1:30"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81979148","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}
Indoor conflagration causes a large number of casualties and property losses worldwide every year. Yet existing indoor fire detection systems either suffer from short sensing range (e.g., ≤ 0.5m using a thermometer), susceptible to interferences (e.g., smoke detector) or high computational and deployment overhead (e.g., cameras, Wi-Fi). This paper proposes HearFire, a cost-effective, easy-to-use and timely room-scale fire detection system via acoustic sensing. HearFire consists of a collocated commodity speaker and microphone pair, which remotely senses fire by emitting inaudible sound waves. Unlike existing works that use signal reflection effect to fulfill acoustic sensing tasks, HearFire leverages sound absorption and sound speed variations to sense the fire due to unique physical properties of flame. Through a deep analysis of sound transmission, HearFire effectively achieves room-scale sensing by correlating the relationship between the transmission signal length and sensing distance. The transmission frame is carefully selected to expand sensing range and balance a series of practical factors that impact the system’s performance. We further design a simple yet effective approach to remove the environmental interference caused by signal reflection by conducting a deep investigation into channel differences between sound reflection and sound absorption. Specifically, sound reflection results in a much more stable pattern in terms of signal energy than sound absorption, which can be exploited to differentiate the channel measurements caused by fire from other interferences. Extensive experiments demonstrate that HireFire enables a maximum 7m sensing range and achieves timely fire detection in indoor environments with up to 99 . 2% accuracy under different experiment configurations.
{"title":"HearFire: Indoor Fire Detection via Inaudible Acoustic Sensing","authors":"Z. Wang","doi":"10.1145/3569500","DOIUrl":"https://doi.org/10.1145/3569500","url":null,"abstract":"Indoor conflagration causes a large number of casualties and property losses worldwide every year. Yet existing indoor fire detection systems either suffer from short sensing range (e.g., ≤ 0.5m using a thermometer), susceptible to interferences (e.g., smoke detector) or high computational and deployment overhead (e.g., cameras, Wi-Fi). This paper proposes HearFire, a cost-effective, easy-to-use and timely room-scale fire detection system via acoustic sensing. HearFire consists of a collocated commodity speaker and microphone pair, which remotely senses fire by emitting inaudible sound waves. Unlike existing works that use signal reflection effect to fulfill acoustic sensing tasks, HearFire leverages sound absorption and sound speed variations to sense the fire due to unique physical properties of flame. Through a deep analysis of sound transmission, HearFire effectively achieves room-scale sensing by correlating the relationship between the transmission signal length and sensing distance. The transmission frame is carefully selected to expand sensing range and balance a series of practical factors that impact the system’s performance. We further design a simple yet effective approach to remove the environmental interference caused by signal reflection by conducting a deep investigation into channel differences between sound reflection and sound absorption. Specifically, sound reflection results in a much more stable pattern in terms of signal energy than sound absorption, which can be exploited to differentiate the channel measurements caused by fire from other interferences. Extensive experiments demonstrate that HireFire enables a maximum 7m sensing range and achieves timely fire detection in indoor environments with up to 99 . 2% accuracy under different experiment configurations.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"28 1","pages":"185:1-185:25"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78973624","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}
Zhigang Yin, M. Liyanage, Abdul-Rasheed Ottun, Souvik Paul, Agustin Zuniga, P. Nurmi, Huber Flores
Hand-grip strength is widely used to estimate muscle strength and it serves as a general indicator of the overall health of a person, particularly in aging adults. Hand-grip strength is typically estimated using dynamometers or specialized force resistant pressure sensors embedded onto objects. Both of these solutions require the user to interact with a dedicated measurement device which unnecessarily restricts the contexts where estimates are acquired. We contribute HIPPO, a novel non-intrusive and opportunistic method for estimating hand-grip strength from everyday interactions with objects. HIPPO re-purposes light sensors available in wearables (e.g., rings or gloves) to capture changes in light reflectivity when people interact with objects. This allows HIPPO to non-intrusively piggyback everyday interactions for health information without affecting the user’s everyday routines. We present two prototypes integrating HIPPO, an early smart glove proof-of-concept, and a further optimized solution that uses sensors integrated onto a ring. We validate HIPPO through extensive experiments and compare HIPPO against three baselines, including a clinical dynamometer. Our results show that HIPPO operates robustly across a wide range of everyday objects, and participants. The force strength estimates correlate with estimates produced by pressure-based devices, and can also determine the correct hand grip strength category with up to 86% accuracy. Our findings also suggest that users prefer our approach to existing solutions as HIPPO blends the estimation with everyday interactions.
{"title":"HIPPO: Pervasive Hand-Grip Estimation from Everyday Interactions","authors":"Zhigang Yin, M. Liyanage, Abdul-Rasheed Ottun, Souvik Paul, Agustin Zuniga, P. Nurmi, Huber Flores","doi":"10.1145/3570344","DOIUrl":"https://doi.org/10.1145/3570344","url":null,"abstract":"Hand-grip strength is widely used to estimate muscle strength and it serves as a general indicator of the overall health of a person, particularly in aging adults. Hand-grip strength is typically estimated using dynamometers or specialized force resistant pressure sensors embedded onto objects. Both of these solutions require the user to interact with a dedicated measurement device which unnecessarily restricts the contexts where estimates are acquired. We contribute HIPPO, a novel non-intrusive and opportunistic method for estimating hand-grip strength from everyday interactions with objects. HIPPO re-purposes light sensors available in wearables (e.g., rings or gloves) to capture changes in light reflectivity when people interact with objects. This allows HIPPO to non-intrusively piggyback everyday interactions for health information without affecting the user’s everyday routines. We present two prototypes integrating HIPPO, an early smart glove proof-of-concept, and a further optimized solution that uses sensors integrated onto a ring. We validate HIPPO through extensive experiments and compare HIPPO against three baselines, including a clinical dynamometer. Our results show that HIPPO operates robustly across a wide range of everyday objects, and participants. The force strength estimates correlate with estimates produced by pressure-based devices, and can also determine the correct hand grip strength category with up to 86% accuracy. Our findings also suggest that users prefer our approach to existing solutions as HIPPO blends the estimation with everyday interactions.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"61 1","pages":"209:1-209:30"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74486798","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}
Zihan Yan, Jiayi Zhou, Wu Yufei, Guanhong Liu, Danli Luo, Zi Zhou, Mi Haipeng, Lingyun Sun, Xiang 'Anthony' Chen, Yang Zhang, Guanyun Wang
Feet are the foundation of our bodies that not only perform locomotion but also participate in intent and emotion expression. Thus, foot gestures are an intuitive and natural form of expression for interpersonal interaction. Recent studies have mostly introduced smart shoes as personal gadgets, while foot gestures used in multi-person foot interactions in social scenarios remain largely unexplored. We present Shoes++, which includes an inertial measurement unit (IMU)-mounted sole and an input vocabulary of social foot-to-foot gestures to support foot-based interaction. The gesture vocabulary is derived and condensed by a set of gestures elicited from a participatory design session with 12 users. We implement a machine learning model in Shoes++ which can recognize two-person and three-person social foot-to-foot gestures with 94.3% and 96.6% accuracies (N=18). In addition, the sole is designed to easily attach to and detach from various daily shoes to support comfortable social foot interaction without taking off the shoes. Based on users’ qualitative feedback, we also found that Shoes++ can support team collaboration and enhance emotion expression, thus making social interactions or interpersonal dynamics more engaging in an expanded design space. Additional Key and smart sole Shoes++: A Smart Detachable Sole for Social Foot-to-foot Interaction. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6, 2, (June 2022),
{"title":"Shoes++: A Smart Detachable Sole for Social Foot-to-foot Interaction","authors":"Zihan Yan, Jiayi Zhou, Wu Yufei, Guanhong Liu, Danli Luo, Zi Zhou, Mi Haipeng, Lingyun Sun, Xiang 'Anthony' Chen, Yang Zhang, Guanyun Wang","doi":"10.1145/3534620","DOIUrl":"https://doi.org/10.1145/3534620","url":null,"abstract":"Feet are the foundation of our bodies that not only perform locomotion but also participate in intent and emotion expression. Thus, foot gestures are an intuitive and natural form of expression for interpersonal interaction. Recent studies have mostly introduced smart shoes as personal gadgets, while foot gestures used in multi-person foot interactions in social scenarios remain largely unexplored. We present Shoes++, which includes an inertial measurement unit (IMU)-mounted sole and an input vocabulary of social foot-to-foot gestures to support foot-based interaction. The gesture vocabulary is derived and condensed by a set of gestures elicited from a participatory design session with 12 users. We implement a machine learning model in Shoes++ which can recognize two-person and three-person social foot-to-foot gestures with 94.3% and 96.6% accuracies (N=18). In addition, the sole is designed to easily attach to and detach from various daily shoes to support comfortable social foot interaction without taking off the shoes. Based on users’ qualitative feedback, we also found that Shoes++ can support team collaboration and enhance emotion expression, thus making social interactions or interpersonal dynamics more engaging in an expanded design space. Additional Key and smart sole Shoes++: A Smart Detachable Sole for Social Foot-to-foot Interaction. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6, 2, (June 2022),","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"8 1","pages":"85:1-85:29"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74092177","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}
Luis Paredes, Ananya Ipsita, J. C. Mesa, Ramses V. Martinez Garrido, K. Ramani
presents StretchAR, wearable straps that exploit touch and stretch as input modalities to interact with the virtual content displayed on smart glasses. StretchAR straps are thin, lightweight, and can be attached to existing garments to enhance users’ interactions in AR. StretchAR straps can withstand strains up to 190% while remaining sensitive to touch inputs. The strap allows the effective combination of these inputs as a mode of interaction with the content displayed through AR widgets, maps, menus, social media, and Internet of Things (IoT) devices. Furthermore, we conducted a user study with 15 participants to determine the potential implications of the use of StretchAR as input modalities when placed on four different body locations (head, chest, forearm, and wrist). This study reveals that StretchAR can be used as an efficient and convenient input modality for smart glasses with a 96% accuracy. Additionally, we provide a collection of 28 interactions enabled by the simultaneous touch–stretch capabilities of StretchAR. Finally, we facilitate recommendation guidelines for the design, fabrication, placement, and possible applications of StretchAR as an interaction modality for AR content displayed on smart glasses. Exploiting as
{"title":"StretchAR: Exploiting Touch and Stretch as a Method of Interaction for Smart Glasses Using Wearable Straps","authors":"Luis Paredes, Ananya Ipsita, J. C. Mesa, Ramses V. Martinez Garrido, K. Ramani","doi":"10.1145/3550305","DOIUrl":"https://doi.org/10.1145/3550305","url":null,"abstract":"presents StretchAR, wearable straps that exploit touch and stretch as input modalities to interact with the virtual content displayed on smart glasses. StretchAR straps are thin, lightweight, and can be attached to existing garments to enhance users’ interactions in AR. StretchAR straps can withstand strains up to 190% while remaining sensitive to touch inputs. The strap allows the effective combination of these inputs as a mode of interaction with the content displayed through AR widgets, maps, menus, social media, and Internet of Things (IoT) devices. Furthermore, we conducted a user study with 15 participants to determine the potential implications of the use of StretchAR as input modalities when placed on four different body locations (head, chest, forearm, and wrist). This study reveals that StretchAR can be used as an efficient and convenient input modality for smart glasses with a 96% accuracy. Additionally, we provide a collection of 28 interactions enabled by the simultaneous touch–stretch capabilities of StretchAR. Finally, we facilitate recommendation guidelines for the design, fabrication, placement, and possible applications of StretchAR as an interaction modality for AR content displayed on smart glasses. Exploiting as","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"9 1","pages":"134:1-134:26"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76663092","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}
Spontaneous selection of IoT devices from the head-mounted device is key for user-centered pervasive interaction. BLEselect enables users to select an unmodified Bluetooth 5.1 compatible IoT device by nodding at, pointing at, or drawing a circle in the air around it. We designed a compact antenna array that fits on a pair of smart glasses to estimate the Angle of Arrival (AoA) of IoT and wrist-worn devices’ advertising signals. We then developed a sensing pipeline that supports all three selection gestures with lightweight machine learning models, which are trained in real-time for both hand gestures. Extensive characterizations and evaluations show that our system is accurate, natural, low-power, and privacy-preserving. Despite the small effective size of the antenna array, our system achieves a higher than 90% selection accuracy within a 3 meters distance in front of the user. In a user study that mimics real-life usage cases, the overall selection accuracy is 96.7% for a diverse set of 22 participants in terms of age, technology savviness, and body structures.
{"title":"BLEselect: Gestural IoT Device Selection via Bluetooth Angle of Arrival Estimation from Smart Glasses","authors":"Tengxiang Zhang, Zitong Lan, Chenren Xu, Yanrong Li, Yiqiang Chen","doi":"10.1145/3569482","DOIUrl":"https://doi.org/10.1145/3569482","url":null,"abstract":"Spontaneous selection of IoT devices from the head-mounted device is key for user-centered pervasive interaction. BLEselect enables users to select an unmodified Bluetooth 5.1 compatible IoT device by nodding at, pointing at, or drawing a circle in the air around it. We designed a compact antenna array that fits on a pair of smart glasses to estimate the Angle of Arrival (AoA) of IoT and wrist-worn devices’ advertising signals. We then developed a sensing pipeline that supports all three selection gestures with lightweight machine learning models, which are trained in real-time for both hand gestures. Extensive characterizations and evaluations show that our system is accurate, natural, low-power, and privacy-preserving. Despite the small effective size of the antenna array, our system achieves a higher than 90% selection accuracy within a 3 meters distance in front of the user. In a user study that mimics real-life usage cases, the overall selection accuracy is 96.7% for a diverse set of 22 participants in terms of age, technology savviness, and body structures.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"280 1","pages":"198:1-198:28"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80136760","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}
Reaching surrounding target objects is difficult for blind and low-vision (BLV) users, affecting their daily life. Based on interviews and exchanges, we propose an unobtrusive wearable system called LiSee to provide BLV users with all-day assistance. Following a user-centered design method, we carefully designed the LiSee prototype, which integrates various electronic components and is disguised as a neckband headphone such that it is an extension of the existing headphone. The top-level software includes a series of seamless image processing algorithms to solve the challenges brought by the unconstrained wearable form so as to ensure excellent real-time performance. Moreover, users are provided with a personalized guidance scheme so that they can use LiSee quickly based on their personal expertise. Finally, a system evaluation and a user study were completed in the laboratory and participants’ homes. The results show that LiSee works robustly, indicating that it can meet the daily needs of most participants to reach surrounding objects.
{"title":"LiSee: A Headphone that Provides All-day Assistance for Blind and Low-vision Users to Reach Surrounding Objects","authors":"Kaixin Chen, Yongzhi Huang, Yicong Chen, Haobin Zhong, Lihua Lin, Lu Wang, Kaishun Wu","doi":"10.1145/3550282","DOIUrl":"https://doi.org/10.1145/3550282","url":null,"abstract":"Reaching surrounding target objects is difficult for blind and low-vision (BLV) users, affecting their daily life. Based on interviews and exchanges, we propose an unobtrusive wearable system called LiSee to provide BLV users with all-day assistance. Following a user-centered design method, we carefully designed the LiSee prototype, which integrates various electronic components and is disguised as a neckband headphone such that it is an extension of the existing headphone. The top-level software includes a series of seamless image processing algorithms to solve the challenges brought by the unconstrained wearable form so as to ensure excellent real-time performance. Moreover, users are provided with a personalized guidance scheme so that they can use LiSee quickly based on their personal expertise. Finally, a system evaluation and a user study were completed in the laboratory and participants’ homes. The results show that LiSee works robustly, indicating that it can meet the daily needs of most participants to reach surrounding objects.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"17 1","pages":"104:1-104:30"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82675803","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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