Proceedings of the 2021 ACM International Symposium on Wearable Computers最新文献_第4页

Using additional training sensors to improve single-sensor complex activity recognition 利用附加训练传感器改进单传感器复杂活动识别

Proceedings of the 2021 ACM International Symposium on Wearable Computers Pub Date : 2021-09-21 DOI: 10.1145/3460421.3480421

P. Lago, Moe Matsuki, Kohei Adachi, Sozo Inoue

引用次数: 6

CalmWear: A Smart Tactile Sensory Stimulation Clothing 一种智能触觉感官刺激服装

Proceedings of the 2021 ACM International Symposium on Wearable Computers Pub Date : 2021-09-21 DOI: 10.1145/3460421.3478829

G. Berk, Ruoyu Zhang, C. Yilmaz

{"title":"CalmWear: A Smart Tactile Sensory Stimulation Clothing","authors":"G. Berk, Ruoyu Zhang, C. Yilmaz","doi":"10.1145/3460421.3478829","DOIUrl":"https://doi.org/10.1145/3460421.3478829","url":null,"abstract":"The emotional and physical health benefits that come by being touched in safe ways are well established. Tactile sensory stimulation has been reported to help calm the arousal in people with Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), Sensory Processing Disorder as well as Anxiety Disorders. Anxiety disorders which effect around 265 million people worldwide can benefit from innovation into design-based, non-pharmacological interventions. Smart clothing and textiles have the potential to not only to sense and collect physiological data about the wearer but also to act on the body with embedded actuation capabilities such as promoting tactile stimuli. This study presents a novel reactive smart clothing that can respond to changes in vital signs of the body with tactile stimulation. CalmWear features tactile actuating through a strategically located and textured air bladder that provides automated and dynamic compression in response to change in heart rate variability and respiration rate as indicators of anxiety. CalmWear offers novel ways of complementing care to empower people with anxiety disorders with its ubiquitous nature and its affordance.","PeriodicalId":395295,"journal":{"name":"Proceedings of the 2021 ACM International Symposium on Wearable Computers","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132406074","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: 6

Wearability and Comfort of Earables During Sleep 睡眠中可穿戴设备的可穿戴性和舒适度

Proceedings of the 2021 ACM International Symposium on Wearable Computers Pub Date : 2021-09-21 DOI: 10.1145/3460421.3480432

Tobias Röddiger, Christian Dinse, M. Beigl

引用次数: 4

Mindless Load Changer: A Method for Manipulating Load Perception by Feedback of Myoelectricity Sensor Information 无脑负荷改变器:一种利用肌电传感器信息反馈操纵负荷感知的方法

Proceedings of the 2021 ACM International Symposium on Wearable Computers Pub Date : 2021-09-21 DOI: 10.1145/3460421.3478816

Kyosuke Futami, Tomoya Seki, Kazuya Murao

引用次数: 6

Assisting Motor Skill Transfer for Dance StudentsUsing Wearable Feedback 使用可穿戴反馈协助舞蹈学生的运动技能转移

Proceedings of the 2021 ACM International Symposium on Wearable Computers Pub Date : 2021-09-21 DOI: 10.1145/3460421.3478817

Steeven Villa, Jasmin Niess, Bettina Eska, A. Schmidt, Tonja Machulla

引用次数: 2

Detecting Freezing of Gait with Earables Trained from VR Motion Capture Data 用VR动作捕捉数据训练的耳机检测步态冻结

Proceedings of the 2021 ACM International Symposium on Wearable Computers Pub Date : 2021-09-21 DOI: 10.1145/3460421.3478821

Nobuyuki Oishi, Benedetta Heimler, Lloyd Pellatt, M. Plotnik, D. Roggen

{"title":"Detecting Freezing of Gait with Earables Trained from VR Motion Capture Data","authors":"Nobuyuki Oishi, Benedetta Heimler, Lloyd Pellatt, M. Plotnik, D. Roggen","doi":"10.1145/3460421.3478821","DOIUrl":"https://doi.org/10.1145/3460421.3478821","url":null,"abstract":"Freezing of Gait (FoG) is a common disabling motor symptom in Parkinson’s Disease (PD). Auditory cueing provided when FoG is detected can help mitigate the condition, for which earables are potentially well suited as they are capable of motion sensing and audio feedback. However, there are no studies so far on FoG detection at the ear. Immersive Virtual Reality (VR) combined with video-based full-body motion capture has been increasingly used to run FoG studies in the medical community. While there are motion capture datasets collected in such an environment, there are no datasets collected from IMU placed at the ear. In this paper, we show how to transfer such motion capture datasets to IMU domain and evaluate the capability of FoG detection from ear position in an immersive VR environment. Using a dataset of 6 PD patients, we compare machine learning-based FoG detection applied to the motion capture data and the virtual IMU. We have achieved an average sensitivity of 80.3% and an average specificity of 87.6% on FoG detection using the virtual earable IMU, which indicates the potential of FoG detection at the ear. This study is a step toward user-studies with earables in the VR setup, prior to conducting research in over-ground walking and everyday life.","PeriodicalId":395295,"journal":{"name":"Proceedings of the 2021 ACM International Symposium on Wearable Computers","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123396687","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

Fast Deep Neural Architecture Search for Wearable Activity Recognition by Early Prediction of Converged Performance 基于收敛性能早期预测的可穿戴活动识别快速深度神经结构搜索

Proceedings of the 2021 ACM International Symposium on Wearable Computers Pub Date : 2021-09-21 DOI: 10.1145/3460421.3478813

Lloyd Pellatt, D. Roggen

{"title":"Fast Deep Neural Architecture Search for Wearable Activity Recognition by Early Prediction of Converged Performance","authors":"Lloyd Pellatt, D. Roggen","doi":"10.1145/3460421.3478813","DOIUrl":"https://doi.org/10.1145/3460421.3478813","url":null,"abstract":"Neural Architecture Search (NAS) has the potential to uncover more performant networks for wearable activity recognition, but a naive evaluation of the search space is computationally expensive. We introduce neural regression methods for predicting the converged performance of a Deep Neural Network (DNN) using validation performance in early epochs and topological and computational statistics. Our approach shows a significant improvement in predicting converged testing performance. We apply this to the optimisation of the convolutional feature extractor of an LSTM recurrent network using NAS with deep Q-learning, optimising the kernel size, number of kernels, number of layers and the connections between layers, allowing for arbitrary skip connections and dimensionality reduction with pooling layers. We find architectures which achieve up to 4% better F1 score on the recognition of gestures in the Opportunity dataset than our implementation of the state of the art model DeepConvLSTM, while reducing the search time by >90% over a random search. This opens the way to rapidly search for well performing dataset-specific architectures.","PeriodicalId":395295,"journal":{"name":"Proceedings of the 2021 ACM International Symposium on Wearable Computers","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128281957","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: 5

Brainwear: Towards Multi-modal Garment Integrated EEG 脑服：实现集成脑电图的多模式服装

Proceedings of the 2021 ACM International Symposium on Wearable Computers Pub Date : 2021-09-21 DOI: 10.1145/3460421.3478814

J. F. Vargas, Bo Zhou, Hymalai Bello, P. Lukowicz

引用次数: 2

IBSync: Intra-body Synchronization of Wearable Devices Using Artificial ECG Landmarks IBSync:使用人工心电地标的可穿戴设备的体内同步

Proceedings of the 2021 ACM International Symposium on Wearable Computers Pub Date : 2021-09-21 DOI: 10.1145/3460421.3478815

Florian Wolling, Cong Dat Huynh, Kristof Van Laerhoven

{"title":"IBSync: Intra-body Synchronization of Wearable Devices Using Artificial ECG Landmarks","authors":"Florian Wolling, Cong Dat Huynh, Kristof Van Laerhoven","doi":"10.1145/3460421.3478815","DOIUrl":"https://doi.org/10.1145/3460421.3478815","url":null,"abstract":"The synchronization of wearable devices in distributed, multi-device systems is a persistent challenge. Particularly machine learning approaches suffer from the devices’ inaccurate clock sources and unmatched time. While the online synchronization based on radio transmission is energy-intensive, offline approaches originated in activity recognition suffer from inaccurate motion patterns. In recent years, intra-body communication emerged as a promising technique that uses the human body as a limited and hence more efficient medium. Due to the absence of commercial platforms, applications are rare and underinvestigated. To boost their development and to enable the precise synchronization, we introduce IBSync and propose to repurpose the ECG sensor in commercial wearable devices to detect artificial signals induced into the skin. The short-time Fourier transform and Pearson’s normalized cross-correlation are used to detect, precisely locate, and assign synchronization landmarks within the measurements. Based on a total of 105 min of recordings, we evaluated the concept and demonstrate its general feasibility with a promising accuracy of 0.203 ± 1.633 samples (1.587 ± 12.755 ms) in typical proximity to the transmitter.","PeriodicalId":395295,"journal":{"name":"Proceedings of the 2021 ACM International Symposium on Wearable Computers","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133862056","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: 5

Simulation of Garment-Embedded Contact Sensor Performance under Motion Dynamics 运动动力学下服装嵌入式接触式传感器性能仿真

Proceedings of the 2021 ACM International Symposium on Wearable Computers Pub Date : 2021-09-21 DOI: 10.1145/3460421.3480423

A. Baronetto, L. Uhlenberg, Dominik Wassermann, O. Amft

引用次数: 4