Proceedings of the 8th Workshop on Body-Centric Computing Systems最新文献

Ergonomical assessment using wearable motion capture system in dockworkers during lashing 码头工人绑扎过程中可穿戴运动捕捉系统的人体工程学评估

Proceedings of the 8th Workshop on Body-Centric Computing Systems Pub Date : 2023-06-18 DOI: 10.1145/3597061.3597258

S. Scataglini, Lenie Denteneer, Nele Struyf, S. Truijen

{"title":"Ergonomical assessment using wearable motion capture system in dockworkers during lashing","authors":"S. Scataglini, Lenie Denteneer, Nele Struyf, S. Truijen","doi":"10.1145/3597061.3597258","DOIUrl":"https://doi.org/10.1145/3597061.3597258","url":null,"abstract":"Work-related injuries involving significant physical loads are of growing concern, as these injuries can negatively impact work productivity, physical health and well-being at work, but also have economic costs due to absenteeism. This observational study aims to quantify the ergonomical assessment of dockworkers using wearable devices to reduce the risk of injuries. Two different gestures are analyzed and observed using a wearable mocap system: Task1A securing the lash bars and Task 1B as lifting and locking process. Additional tests and questionnaire were also requested: FMS, HHD, RAND-36, ODI, and TSK questionnaires. As result, it was found a large spread between the maximum and minimum degrees and the range in which they are working among the different dockworkers. Despite the large heterogeneity, we found for task 1B that the wrist was held in extreme positions, and a significant correlation was found between the time and standard deviation of the low back (R = 0.45, p = 0.045). In addition, all participants reported having complaints; 66% had kinesiophobia, 60% were overweight, and the mean score on the FMS was 15. The occurrence of fatigue, complaints, kinesiophobia, a challenging work environment, or an unhealthy lifestyle may lead to altered movement patterns, which could possibly increase the risk of injury. Based on the results, we recommend implementing a warm-up and an individualized prevention program to reduce the risk of injury.","PeriodicalId":126710,"journal":{"name":"Proceedings of the 8th Workshop on Body-Centric Computing Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134277902","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

Towards Environment-Independent Activity Recognition Using Wi-Fi CSI with an Encoder-Decoder Network 基于编码器-解码器网络的Wi-Fi CSI实现环境无关的活动识别

Proceedings of the 8th Workshop on Body-Centric Computing Systems Pub Date : 2023-06-18 DOI: 10.1145/3597061.3597261

Yusuke Sugimoto, Hamada Rizk, A. Uchiyama, Hirozumi Yamaguchi

{"title":"Towards Environment-Independent Activity Recognition Using Wi-Fi CSI with an Encoder-Decoder Network","authors":"Yusuke Sugimoto, Hamada Rizk, A. Uchiyama, Hirozumi Yamaguchi","doi":"10.1145/3597061.3597261","DOIUrl":"https://doi.org/10.1145/3597061.3597261","url":null,"abstract":"Human Activity Recognition (HAR) has attracted considerable attention in recent years due to its potential applications in healthcare, smart homes, and security. Wi-Fi Channel State Information (CSI) is a promising sensor modality for HAR, providing a device-free and low-cost solution. However, building environment-independent models for HAR using Wi-Fi CSI remains a significant challenge. In this paper, we present a deep learning-based activity recognition system that exploits CSI measurements obtained from one or more environments to deliver consistent and accurate performance even in unseen environments. Our system employs a multi-task learning approach that is based on an encoder-decoder network architecture. This enables the encoder part of this architecture to mitigate the environment-dependent factors and extract a rich and environment-invariant representation. To evaluate the proposed system, we collected CSI samples for six activities pursued by three participants in four distinct environments. The results demonstrate the efficacy of the proposed system in achieving environment-independent HAR with an average accuracy of 80%. Additionally, the results validate the superiority of our method over environment-specific models by a minimum margin of 6% in cases of limited data.","PeriodicalId":126710,"journal":{"name":"Proceedings of the 8th Workshop on Body-Centric Computing Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134344328","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

Imputation of Human Mobility Data for Comprehensive Risk Models 综合风险模型中人员流动数据的代入

Proceedings of the 8th Workshop on Body-Centric Computing Systems Pub Date : 2023-06-18 DOI: 10.1145/3597061.3597260

Shashee Kumari, Sakyajit Bhattacharya, Arnab Chatterjee, Avik Ghose

引用次数: 0

TinyPuff: Automated design of Tiny Smoking Puff Classifiers for Body Worn Devices TinyPuff:用于身体穿戴设备的自动设计的微型吸烟烟雾分类器

Proceedings of the 8th Workshop on Body-Centric Computing Systems Pub Date : 2023-06-18 DOI: 10.1145/3597061.3597259

Shalini Mukhopadhyay, Swarnava Dey, Avik Ghose

{"title":"TinyPuff: Automated design of Tiny Smoking Puff Classifiers for Body Worn Devices","authors":"Shalini Mukhopadhyay, Swarnava Dey, Avik Ghose","doi":"10.1145/3597061.3597259","DOIUrl":"https://doi.org/10.1145/3597061.3597259","url":null,"abstract":"Smoking is a significant cause of death and deterioration of health worldwide, affecting active and passive smokers. Cessation of smoking contributes to an essential health and wellness application owing to the broad range of health problems such as cancer, hypertension, and several cardiopulmonary diseases. Personalized smoking-cessation applications can be very effective in helping users to stop smoking if there are detections and interventions done at the right time. This requires real-time detection of smoking puffs. Such applications are made feasible by day-long monitoring and smoking puff detection from unobtrusive devices such as wearables. This paper proposes a deep inference technique for the real-time detection of smoking puffs on a wearable device. We show that a simple, sequential Convolutional Neural Network (CNN) using only 6-axis Inertial signals can be utilized in place of complex and resource-consuming Deep Learning models. The accuracy achieved is comparable to State-of-the-Art techniques with an F1 score of 0.81, although the model size is tiny - 114 kB. Such small models can be deployed on the lowest configuration hardware platforms, achieving accurate but high-speed, low-power inference on conventional smartwatches. We ensure that the auto-designed models are directly compatible with resource-constrained platforms such as TensorFlow Lite and TensorFlow Lite for Microcontrollers (TFLM) without requiring further use of model reduction and optimization techniques. Our proposed approach will allow affordable wearable device manufacturers to run smoking detection on their devices, as it is tiny enough to fit TinyML platforms and is only dependent on IMU sensors that are universally available.","PeriodicalId":126710,"journal":{"name":"Proceedings of the 8th Workshop on Body-Centric Computing Systems","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116845401","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: 2

Assessment of Mental Workloads using Differential Dermal Potentials Recorded from in and around Ear 利用耳内及耳周皮肤电位差异评估心理负荷

Proceedings of the 8th Workshop on Body-Centric Computing Systems Pub Date : 2023-06-18 DOI: 10.1145/3597061.3597257

D. Jaiswal, D. Chatterjee, Arindam Sarkar, M. S, R. K. Ramakrishnan, Arpan Pal, R. Ghosh

{"title":"Assessment of Mental Workloads using Differential Dermal Potentials Recorded from in and around Ear","authors":"D. Jaiswal, D. Chatterjee, Arindam Sarkar, M. S, R. K. Ramakrishnan, Arpan Pal, R. Ghosh","doi":"10.1145/3597061.3597257","DOIUrl":"https://doi.org/10.1145/3597061.3597257","url":null,"abstract":"Differential dermal potentials is a non-invasive bio-potential signal acquired from the skin surface is a relatively new mode for human activity and behavior monitoring. Literature suggests acquiring differential dermal potentials from either hand or leg, which might be uncomfortable for long term usage. In the present work, for the first time we have acquired the differential dermal potentials from in and around the ear and have used that for monitoring mental workload. Experimental data were acquired from ear lobes and ear canals using two different devices while the participants were engaged in a mental arithmetic task. Thereafter, the signals are analyzed and a set of the most discriminating features are identified. Using these features, a cognitive load prediction model is developed. Results show that, for rest vs. load classification, we achieved an accuracy of 89% for ear canal data and 94.2% for ear lobe data. We also observed a positive co-occurrence of signals collected from these two locations. Thus, our proposed approach can be used for monitoring mental workload condition in real-life applications and is suitable for long term usage.","PeriodicalId":126710,"journal":{"name":"Proceedings of the 8th Workshop on Body-Centric Computing Systems","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114072133","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

Proceedings of the 8th Workshop on Body-Centric Computing Systems 第八届以身体为中心的计算系统研讨会论文集

Proceedings of the 8th Workshop on Body-Centric Computing Systems Pub Date : 1900-01-01 DOI: 10.1145/3597061

引用次数: 0