Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.最新文献_第7页

BLEselect: Gestural IoT Device Selection via Bluetooth Angle of Arrival Estimation from Smart Glasses BLEselect:通过智能眼镜的蓝牙到达角度估计进行手势物联网设备选择

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Pub Date : 2022-01-01 DOI: 10.1145/3569482

Tengxiang Zhang, Zitong Lan, Chenren Xu, Yanrong Li, Yiqiang Chen

引用次数: 1

HIPPO: Pervasive Hand-Grip Estimation from Everyday Interactions 河马:从日常互动中估计普遍的握力

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Pub Date : 2022-01-01 DOI: 10.1145/3570344

Zhigang Yin, M. Liyanage, Abdul-Rasheed Ottun, Souvik Paul, Agustin Zuniga, P. Nurmi, Huber Flores

{"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}

引用次数: 3

EarSpiro: Earphone-based Spirometry for Lung Function Assessment EarSpiro:用于肺功能评估的耳机肺活量测定法

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Pub Date : 2022-01-01 DOI: 10.1145/3569480

Wentao Xie, Qing Hu, Jin Zhang, Qian Zhang

{"title":"EarSpiro: Earphone-based Spirometry for Lung Function Assessment","authors":"Wentao Xie, Qing Hu, Jin Zhang, Qian Zhang","doi":"10.1145/3569480","DOIUrl":"https://doi.org/10.1145/3569480","url":null,"abstract":"Spirometry is the gold standard for evaluating lung functions. Recent research has proposed that mobile devices can measure lung function indices cost-efficiently. However, these designs fall short in two aspects. First, they cannot provide the flow-volume (F-V) curve, which is more informative than lung function indices. Secondly, these solutions lack inspiratory measurement, which is sensitive to lung diseases such as variable extrathoracic obstruction. In this paper, we present EarSpiro, an earphone-based solution that interprets the recorded airflow sound during a spirometry test into an F-V curve, including both the expiratory and inspiratory measurements. EarSpiro leverages a convolutional neural network (CNN) and a recurrent neural network (RNN) to capture the complex correlation between airflow sound and airflow speed. Meanwhile, EarSpiro adopts a clustering-based segmentation algorithm to track the weak inspiratory signals from the raw audio recording to enable inspiratory measurement. We also enable EarSpiro with daily mouthpiece-like objects such as a funnel using transfer learning and a decoder network with the help of only a few true lung function indices from the user. Extensive experiments with 60 subjects show that EarSpiro achieves mean errors of 0 . 20 𝐿 / 𝑠 and 0 . 42 𝐿 / 𝑠 for expiratory and inspiratory flow rate estimation, and 0 . 61 𝐿 / 𝑠 and 0 . 83 𝐿 / 𝑠 for expiratory and inspiratory F-V curve estimation. The mean correlation coefficient between the estimated F-V curve and the true one is 0 . 94. The mean estimation error for four common lung function indices is 7 . 3%.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"111 1","pages":"188:1-188:27"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77870686","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}

引用次数: 0

HaptiDrag: A Device with the Ability to Generate Varying Levels of Drag (Friction) Effects on Real Surfaces HaptiDrag:一种能够在真实表面上产生不同水平的拖动(摩擦)效果的设备

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Pub Date : 2022-01-01 DOI: 10.1145/3550310

Abhijeet Mishra, Piyush Kumar, Jainendra Shukla, Aman Parnami

引用次数: 1

StudentSADD: Rapid Mobile Depression and Suicidal Ideation Screening of College Students during the Coronavirus Pandemic 新型冠状病毒大流行期间大学生移动抑郁与自杀意念的快速筛查

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Pub Date : 2022-01-01 DOI: 10.1145/3534604

M. L. Tlachac, Ricardo Flores, Miranda Reisch, Rimsha Kayastha, -. Ninatau, Rich, V. Melican, Connor Bruneau, H. Caouette, E. Toto, E. Rundensteiner

{"title":"StudentSADD: Rapid Mobile Depression and Suicidal Ideation Screening of College Students during the Coronavirus Pandemic","authors":"M. L. Tlachac, Ricardo Flores, Miranda Reisch, Rimsha Kayastha, -. Ninatau, Rich, V. Melican, Connor Bruneau, H. Caouette, E. Toto, E. Rundensteiner","doi":"10.1145/3534604","DOIUrl":"https://doi.org/10.1145/3534604","url":null,"abstract":"The growing prevalence of depression and suicidal ideation among college students further exacerbated by the Coronavirus pandemic is alarming, highlighting the need for universal mental illness screening technology. With traditional screening questionnaires too burdensome to achieve universal screening in this population, data collected through mobile applications has the potential to rapidly identify at-risk students. While prior research has mostly focused on collecting passive smartphone modalities from students, smartphone sensors are also capable of capturing active modalities. The general public has demonstrated more willingness to share active than passive modalities through an app, yet no such dataset of active mobile modalities for mental illness screening exists for students. Knowing which active modalities hold strong screening capabilities for student populations is critical for developing targeted mental illness screening technology. Thus, we deployed a mobile application to over 300 students during the COVID-19 pandemic to collect the Student Suicidal Ideation and Depression Detection (StudentSADD) dataset. We report on a rich variety of machine learning models including cutting-edge multimodal pretrained deep learning classifiers on active text and voice replies to screen for depression and suicidal ideation. This unique StudentSADD dataset is a valuable resource for the community for developing mobile mental illness screening tools. © 2022 ACM.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"104 1","pages":"76:1-76:32"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80849565","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}

引用次数: 4

AdaMICA: Adaptive Multicore Intermittent Computing AdaMICA:自适应多核间歇计算

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Pub Date : 2022-01-01 DOI: 10.1145/3550304

K. Akhunov, K. Yıldırım

引用次数: 6

EarIO: A Low-power Acoustic Sensing Earable for Continuously Tracking Detailed Facial Movements EarIO:一种低功耗声学传感耳机，用于连续跟踪详细的面部运动

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Pub Date : 2022-01-01 DOI: 10.1145/3534621

Ke Li, Ruidong Zhang, Bo Li, François Guimbretière, Cheng Zhang

{"title":"EarIO: A Low-power Acoustic Sensing Earable for Continuously Tracking Detailed Facial Movements","authors":"Ke Li, Ruidong Zhang, Bo Li, François Guimbretière, Cheng Zhang","doi":"10.1145/3534621","DOIUrl":"https://doi.org/10.1145/3534621","url":null,"abstract":"This paper presents EarIO, an AI-powered acoustic sensing technology that allows an earable (e.g., earphone) to continuously track facial expressions using two pairs of microphone and speaker (one on each side), which are widely available in commodity earphones. It emits acoustic signals from a speaker on an earable towards the face. Depending on facial expressions, the muscles, tissues, and skin around the ear would deform differently, resulting in unique echo profiles in the reflected signals captured by an on-device microphone. These received acoustic signals are processed and learned by a customized deep learning pipeline to continuously infer the full facial expressions represented by 52 parameters captured using a TruthDepth camera. Compared to similar technologies, it has significantly lower power consumption, as it can sample at 86 Hz with a power signature of 154 mW. A user study with 16 participants under three different scenarios, showed that EarIO can reliably estimate the detailed facial movements when the participants were sitting, walking or after remounting the device. Based on the encouraging results, we further discuss the potential opportunities and challenges on applying EarIO on future ear-mounted wearables.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"2013 1","pages":"62:1-62:24"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86279527","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}

引用次数: 14

ThumbAir: In-Air Typing for Head Mounted Displays ThumbAir:在空气中键入头戴式显示器

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Pub Date : 2022-01-01 DOI: 10.1145/3569474

Hyunjae Gil, Ian Oakley

{"title":"ThumbAir: In-Air Typing for Head Mounted Displays","authors":"Hyunjae Gil, Ian Oakley","doi":"10.1145/3569474","DOIUrl":"https://doi.org/10.1145/3569474","url":null,"abstract":"Typing while wearing a standalone Head Mounted Display (HMD)—systems without external input devices or sensors to support text entry—is hard. To address this issue, prior work has used external trackers to monitor finger movements to support in-air typing on virtual keyboards. While performance has been promising, current systems are practically infeasible: finger movements may be visually occluded from inside-out HMD based tracking systems or, otherwise, awkward and uncomfortable to perform. To address these issues, this paper explores an alternative approach. Taking inspiration from the prevalence of thumb-typing on mobile phones, we describe four studies exploring, defining and validating the performance of ThumbAir, an in-air thumb-typing system implemented on a commercial HMD. The first study explores viable target locations, ultimately recommending eight targets sites. The second study collects performance data for taps on pairs of these targets to both inform the design of a target selection procedure and also support a computational design process to select a keyboard layout. The final two studies validate the selected keyboard layout in word repetition and phrase entry tasks, ultimately achieving final WPMs of 27.1 and 13.73. Qualitative data captured in the final study indicate that the discreet movements required to operate ThumbAir, in comparison to the larger scale finger and hand motions used in a baseline design from prior work, lead to reduced levels of perceived exertion and physical demand and are rated as acceptable for use in a wider range of social situations.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"21 1","pages":"164:1-164:30"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79256287","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}

引用次数: 1

Lumos: An Open-Source Device for Wearable Spectroscopy Research Lumos:用于可穿戴光谱研究的开源设备

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Pub Date : 2022-01-01 DOI: 10.1145/3569502

Amanda Watson, Claire Kendell, Anush Lingamoorthy, Insup Lee, James Weimer

引用次数: 1

BodyTrak: Inferring Full-body Poses from Body Silhouettes Using a Miniature Camera on a Wristband BodyTrak:使用腕带上的微型相机从身体轮廓推断全身姿势

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Pub Date : 2022-01-01 DOI: 10.1145/3552312

Hyunchul Lim, Yaxuan Li, Matthew Dressa, Fangwei Hu, Jae Hoon Kim, Ruidong Zhang, Cheng Zhang

引用次数: 4