Proceedings of the 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare最新文献

ARD: accurate and reliable fall detection with using a single wearable inertial sensor ARD:使用单个可穿戴惯性传感器进行准确可靠的跌倒检测

Proceedings of the 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare Pub Date : 2022-10-17 DOI: 10.1145/3556551.3561189

Li Zhang, Qiuyu Wang, Huilin Chen, Jinhui Bao, Jingao Xu, Danyang Li

{"title":"ARD: accurate and reliable fall detection with using a single wearable inertial sensor","authors":"Li Zhang, Qiuyu Wang, Huilin Chen, Jinhui Bao, Jingao Xu, Danyang Li","doi":"10.1145/3556551.3561189","DOIUrl":"https://doi.org/10.1145/3556551.3561189","url":null,"abstract":"Accidental fall is one of the major factors threatening the health of the elderly, which promotes the considerable development of fall detection technology. In our study, a novel method is proposed to detect falls prior to impact during walking. In terms of data collection, acceleration and angular velocity data are collected using a single sensor. By extracting distinctive features, our design goal is to accurately identify fall behavior at an early stage. To improve detection accuracy and reduce false alarms, a classifier based on the joint feature of accelerations and Euler angles (JFAE) analysis is developed. With the support vector machine (SVM) classifier, human activities are classified into eight categories: going upstairs, going downstairs, running, walking, falling forward, falling backward, falling left, and falling right. Not only can it achieve a sensitivity of 96.8% and precision of 96.75%, but also the method we proposed can achieve a high accuracy for the classifier. Compared with the method based on single feature, the method based on multiple features achieves a better performance. The preliminary results indicate that our study has potential application in a fall injury prevention system.","PeriodicalId":202226,"journal":{"name":"Proceedings of the 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134417190","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

RPAA RPAA

Proceedings of the 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare Pub Date : 2022-10-17 DOI: 10.1145/3556551.3561190

Rui Cao, Guohua Liu

{"title":"RPAA","authors":"Rui Cao, Guohua Liu","doi":"10.1145/3556551.3561190","DOIUrl":"https://doi.org/10.1145/3556551.3561190","url":null,"abstract":"Detection of R waves from ECG signals is of great importance yet challenging for the diagnosis of cardiovascular diseases due to the noise. In this paper, an anti-noise R Peak Annotation Algorithm (RPAA) is proposed. In RPAA, the detection of the R peak is firstly transformed into a distance optimization problem, with the goal of learning the distance between all data points in the ECG recording and the nearest R peak. Then we design a U-Net-based neural network which is a set of symmetrical encoder and decoder to predict the distance of each point. The encoder extracts the deep feature representation of the signal through down-sampling. And the decoder fuses the encoder's same-dimensional features and performs up-sampling to predict the distance between each data point and the nearest R peak. Four parallel convolutions are employed to extract features at different scales, and the data flows across layers through a short-cut connected residual structure. The Squeeze-and-Excitation module is incorporated to strengthen the features extracted by the previous layer to improve the performance of the annotation algorithm. For the detection of R peaks in abnormal ECG signals with high noise, the RPAA annotation algorithm obtains a precision rate of 99.56%, a recall rate of 98.29%, and a F1 score of 0.9892. For the detection of R peaks from ECG signals with a signal-to-noise ratio of 0, the RPAA annotation algorithm has a F1 score of 0.8946. Experiments conducted on cross-database also verify that the RPAA algorithm has a high generalization ability.","PeriodicalId":202226,"journal":{"name":"Proceedings of the 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115078862","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

GPSLAM

Proceedings of the 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare Pub Date : 2022-10-17 DOI: 10.1145/3556551.3561188

R. Guan, Mengchao Li, Zhi Li, Caifa Zhou, M. Butt

引用次数: 1

AI enabled RPM for mental health facility 为精神健康机构启用人工智能RPM

Proceedings of the 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare Pub Date : 2022-10-17 DOI: 10.1145/3556551.3561191

T. Shaik, Xiaohui Tao, Niall Higgins, Haoran Xie, R. Gururajan, Xujuan Zhou

{"title":"AI enabled RPM for mental health facility","authors":"T. Shaik, Xiaohui Tao, Niall Higgins, Haoran Xie, R. Gururajan, Xujuan Zhou","doi":"10.1145/3556551.3561191","DOIUrl":"https://doi.org/10.1145/3556551.3561191","url":null,"abstract":"Mental healthcare is one of the prominent parts of the healthcare industry with alarming concerns related to patients' depression, stress leading to self-harm and threat to fellow patients and medical staff. To provide a therapeutic environment for both patients and staff, aggressive or agitated patients need to be monitored remotely and track their vital signs and physical activities continuously. Remote patient monitoring (RPM) using non-invasive technology could enable contactless monitoring of acutely ill patients in a mental health facility. Enabling the RPM system with AI unlocks a predictive environment in which future vital signs of the patients can be forecasted. This paper discusses an AI-enabled RPM system framework with a non-invasive digital technology RFID using its in-built NCS mechanism to retrieve vital signs and physical actions of patients. Based on the retrieved time series data, future vital signs of patients for the upcoming 3 hours and classify their physical actions into 10 labelled physical activities. This framework assists to avoid any unforeseen clinical disasters and take precautionary measures with medical intervention at right time. A case study of a middle-aged PTSD patient treated with the AI-enabled RPM system is demonstrated in this study.","PeriodicalId":202226,"journal":{"name":"Proceedings of the 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114349346","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

Wifi-based robust indoor localization for daily activity monitoring 基于wifi的鲁棒室内定位，用于日常活动监测

Proceedings of the 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare Pub Date : 2022-10-17 DOI: 10.1145/3556551.3561187

Sai Deepika Regani, Yuqian Hu, Beibei Wang, K. Liu

{"title":"Wifi-based robust indoor localization for daily activity monitoring","authors":"Sai Deepika Regani, Yuqian Hu, Beibei Wang, K. Liu","doi":"10.1145/3556551.3561187","DOIUrl":"https://doi.org/10.1145/3556551.3561187","url":null,"abstract":"Achieving indoor localization enables several intelligent home applications, such as monitoring overall activities of daily living (ADL) and triggering location-specific IoT devices. In addition, ADL information further facilitates physical and mental health monitoring and extracting valuable activity insights. While many approaches are proposed to attack this problem, WiFi-based solutions are widely celebrated due to their ubiquity and privacy protection. However, current WiFi-based localization approaches either focus on fine-grained target localization demanding high calibration efforts or cannot localize multiple people at the coarser level, making them unfit for robust ADL applications. In this work, we propose a robust WiFi-based room/zone-level localization solution that is calibration-free, device-free(passive), and built with commercial WiFi chipsets. We extract features indicative of the motion and breathing patterns, thus detecting and localizing a person even when there is only subtle physical movement. Furthermore, we used the correlation between the movement patterns to break ambiguous location scenarios. As a result, we achieved an average detection rate of 96.13%, including different activity levels, and localization accuracy of 98.5% in experiments performed across different environments.","PeriodicalId":202226,"journal":{"name":"Proceedings of the 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130727957","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

Brain tumor segmentation based on CBAM-TransUNet 基于CBAM-TransUNet的脑肿瘤分割

Proceedings of the 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare Pub Date : 2022-10-17 DOI: 10.1145/3556551.3561192

Xingxin Chen, Lei Yang

{"title":"Brain tumor segmentation based on CBAM-TransUNet","authors":"Xingxin Chen, Lei Yang","doi":"10.1145/3556551.3561192","DOIUrl":"https://doi.org/10.1145/3556551.3561192","url":null,"abstract":"Brain tumor is one of the most serious brain diseases, and accurate brain tumor segmentation is crucial in clinical planning treatment and evaluating treatment outcomes in brain tumor patients. In this paper, we propose a 3D visual transducer model (CBAM-TransUNet) that incorporates an attention mechanism for 3D multimodal brain tumor edge detection and segmentation, to improve the accuracy of brain tumor segmentation. In our proposed model based on the framework of the U-Net model (Ronneberger O et al., 2015), Swin Transformer module (LIU Z et al., 2021) is introduced in the process of the encoder and decoder of the model, and the convolution block attention module (WOOS et al., 2018) is applied at the bottleneck layer. Comprehensive experiments are implemented on the BraTS 2021 dataset and it shows that the proposed model obtains competitive results: the Dice coefficients of whole tumor, core tumor and enhanced tumor segmentation are 93.08%, 91.49% and 87.76%, respectively, and the other 95% Hausdorff distances are 2.93mm, 4.20mm, 4.91mm. The proposed CBAM-TransUNet model can effectively improve the accuracy of brain tumor segmentation.","PeriodicalId":202226,"journal":{"name":"Proceedings of the 1st ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare","volume":"1 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132077545","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