2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)最新文献_第2页

Body Movement Monitoring for Parkinson’s Disease Patients Using A Smart Sensor Based Non-Invasive Technique 基于非侵入性技术的智能传感器在帕金森病患者身体运动监测中的应用

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531197

S. Soltaninejad, Andres Rosales-Castellanos, F. Ba, M. Ibarra-Manzano, I. Cheng

{"title":"Body Movement Monitoring for Parkinson’s Disease Patients Using A Smart Sensor Based Non-Invasive Technique","authors":"S. Soltaninejad, Andres Rosales-Castellanos, F. Ba, M. Ibarra-Manzano, I. Cheng","doi":"10.1109/HealthCom.2018.8531197","DOIUrl":"https://doi.org/10.1109/HealthCom.2018.8531197","url":null,"abstract":"There have been increasing interests in recent years on using smart sensor technology, e.g., Kinect and Leap Motion, to capture and analyze human body movements, with the goal to benefit not only games, but also health care and rehab applications. We propose a non-invasive approach using movement data captured from Kinect to monitor motor deficits of Parkinson’s disease (PD) patients. We captured and evaluated simple exercises, normally performed in rehabilitation sessions by physical therapist: Stride Length, Tremor and Timed Up & Go (TUG). The standard medical UPDRS scale is used by a physical therapist to determine the level of severity as the ground truth. The general framework after getting the motion data includes two steps feature extraction from the kinematic motion data, and classification using random forest (RF) (for the stride length and tremor data) and K-means (for the TUG data). Our technique was validated by inviting a group of subjects whose kinematic data are used for PD motion analysis. The experimental results demonstrate the high accuracy of our approach in the assessment of PD using kinematic motion data. Our technique is also suitable in a remote monitoring environment, where data collected can be transmitted to experts for assessment.","PeriodicalId":232709,"journal":{"name":"2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130795677","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

Measuring inconsistent diagnoses 测量不一致诊断

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531146

Diana Costa, M. Martins

引用次数: 0

Classification of grasping tasks based on EEG-EMG coherence 基于EEG-EMG一致性的抓取任务分类

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531140

Giulia Cisotto, A. V. Guglielmi, L. Badia, A. Zanella

引用次数: 16

Cooperative Note-Taking in Psychotherapy Sessions: An Evaluation of the Therapist’s User Experience with Tele-Board MED 心理治疗过程中的合作笔记:远程MED对治疗师用户体验的评价

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531085

Anja Perlich, C. Meinel

{"title":"Cooperative Note-Taking in Psychotherapy Sessions: An Evaluation of the Therapist’s User Experience with Tele-Board MED","authors":"Anja Perlich, C. Meinel","doi":"10.1109/HealthCom.2018.8531085","DOIUrl":"https://doi.org/10.1109/HealthCom.2018.8531085","url":null,"abstract":"In the course of patient treatments, psychotherapists aim to meet the challenges of being both a trusted, knowledgeable conversation partner and a diligent documentalist. We are developing the digital whiteboard system Tele-Board MED (TBM), which allows the therapist to take digital notes during the session together with the patient. This study investigates what therapists are experiencing when they document with TBM in patient sessions for the first time and whether this documentation saves them time when writing official clinical documents. As the core of this study, we conducted four anamnesis session dialogues with behavior psychotherapists and volunteers acting in the role of patients. Following a mixed-method approach, the data collection and analysis involved self-reported emotion samples, user experience curves and questionnaires. We found that even in the very first patient session with TBM, therapists come to feel comfortable, develop a positive feeling and can concentrate on the patient. Regarding administrative documentation tasks, we found with the TBM report generation feature the therapists save 60% of the time they normally spend on writing case reports to the health insurance.","PeriodicalId":232709,"journal":{"name":"2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114600604","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

Outage performance of time switching energy harvesting wireless sensor networks deploying NOMA 采用NOMA的时间交换能量采集无线传感器网络的停机性能

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531184

Hoang-Sy Nguyen, Thanh-Sang Nguyen, P. Tin, M. Voznák

引用次数: 7

Design and Optimization of an Autonomous, Ambulatory Cardiac Event Monitor 自主动态心脏事件监测仪的设计与优化

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531180

B. Massot, F. Hutu, C. Géhin, N. Noury

{"title":"Design and Optimization of an Autonomous, Ambulatory Cardiac Event Monitor","authors":"B. Massot, F. Hutu, C. Géhin, N. Noury","doi":"10.1109/HealthCom.2018.8531180","DOIUrl":"https://doi.org/10.1109/HealthCom.2018.8531180","url":null,"abstract":"Wearable sensors for health monitoring can enable the early detection of various symptoms, and hence rapid reme- dial actions may be undertaken. In particular, the monitoring of cardiac events by using such wearable sensors can provide real- time and more relevant diagnosis of cardiac arrhythmias than classical solutions. However, such devices usually use batteries, which require regular recharging to ensure long-term measure- ments. We therefore designed and evaluated a connected sensor for the ambulatory monitoring of cardiac events, which can be used as an autonomous device without the need of a battery. Even when using off-the-shelf, low-cost integrated circuits, by optimizing both the hardware and software embedded in the device, we were able to reduce the energy consumption of the entire system to below 0.4 mW while measuring and storing the ECG on a non-volatile memory. Moreover, in this paper, a power-management circuit able to store energy collected from the radio communication interface is proposed, able to make the connected sensor fully autonomous. Initial results show that this sensor could be suitable for a truly continuous and long-term monitoring of cardiac events.","PeriodicalId":232709,"journal":{"name":"2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116856046","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

Healthcom 2018 Index Healthcom 2018指数

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/healthcom.2018.8531175

引用次数: 0

Rat Cortical Layers Classification extracting Evoked Local Field Potential Images with Implanted Multi-Electrode Sensor 植入多电极传感器的大鼠皮层层分类提取诱发局部场电位图像

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531084

Xiaying Wang, M. Magno, L. Cavigelli, M. Mahmud, C. Cecchetto, S. Vassanelli, L. Benini

{"title":"Rat Cortical Layers Classification extracting Evoked Local Field Potential Images with Implanted Multi-Electrode Sensor","authors":"Xiaying Wang, M. Magno, L. Cavigelli, M. Mahmud, C. Cecchetto, S. Vassanelli, L. Benini","doi":"10.1109/HealthCom.2018.8531084","DOIUrl":"https://doi.org/10.1109/HealthCom.2018.8531084","url":null,"abstract":"One of the most ambitious goals of neuroscience and its neuroprosthetic applications is to interface intelligent electronic devices with the biological brain to cure neurological diseases. This emerging research field builds on our growing understanding of brain circuits and on recent technological advances in miniaturization of implantable multi-electrode-arrays (MEAs) to record brain signals at high spatiotemporal resolution. Data processing is needed to extract useful information from the recorded neural activity to better understand the function of underlying neural circuits and, in perspective, to operate neuroprosthetic devices. In this context, machine learning approaches are increasingly used in many application scenarios. This paper focuses on processing data of evoked local field potentials (LFPs) recorded from the rat barrel cortex using a miniaturized 16×16 MEA. We evaluated machine learning algorithms and trained an optimized classifier to detect at which cortical depth the neural activity is measured. We demonstrate with experimental results that machine learning can be applied successfully to noisy single-trial LFPs offering up to 99.11% of test accuracy in classifying signals acquired from different cortical layers. As such, the method is a very promising starting point toward real-time decoding of cerebral activities with low power consumption digital processors for brain-machine interfacing and neuroprosthetic applications.","PeriodicalId":232709,"journal":{"name":"2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128477939","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

Comparison of home blood pressure measurement devices in real conditions 家用血压计在实际情况下的比较

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531139

J. Havlík, M. Susankova

引用次数: 0

Similarity Recognition of Interval-Based Sleep Data 基于间隔的睡眠数据相似性识别

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531177

Marc Haßler, Andreas Burgdorf, Christian Kohlschein, Tobias Meisen

引用次数: 1