2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)最新文献_第4页

From the lab to the real-world: An investigation on the influence of human movement on Emotion Recognition using physiological signals 从实验室到现实世界:利用生理信号研究人体运动对情绪识别的影响

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2017-03-13 DOI: 10.1109/PERCOMW.2017.7917586

Yaqian Xu, I. Hübener, Ann-Kathrin Seipp, Sandra Ohly, K. David

{"title":"From the lab to the real-world: An investigation on the influence of human movement on Emotion Recognition using physiological signals","authors":"Yaqian Xu, I. Hübener, Ann-Kathrin Seipp, Sandra Ohly, K. David","doi":"10.1109/PERCOMW.2017.7917586","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917586","url":null,"abstract":"The recognition of human emotions using physiological signals such as Electrodermal Activity (EDA), Electrocardiogram (ECG) or Electromyography (EMG), has been extensively researched in the past attracting a lot of interest during the last few decades. Although showing a relatively satisfactory performance under lab conditions, Emotion Recognition (ER) systems using physiological signals are not widely used in real-world scenarios. One important fact is that, in the real world, physiological signals may be influenced by human movement and therefore, they cannot be used as a unique indicative of emotions. In this paper, we investigate the influence of human movement on ER using physiological signals. We compare different measures of emotion before and after a test person has performed some physical activity (e.g. walking, going upstairs). We discuss the main differences between recognizing emotions in the lab and the real world and provide new insights into the development of ER systems in real-world scenarios.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117235105","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: 21

Trainwear: A real-time assisted training feedback system with fabric wearable sensors Trainwear:一种带有织物可穿戴传感器的实时辅助训练反馈系统

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2017-03-13 DOI: 10.1109/PERCOMW.2017.7917531

Bo Zhou, G. Bahle, Lorenzo Furg, Monit Shah Singh, H. Cruz, P. Lukowicz

引用次数: 7

On the applicability of clinical observation tools for human activity annotation 论临床观察工具在人体活动注释中的适用性

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2017-03-13 DOI: 10.1109/PERCOMW.2017.7917545

Frank Krüger, Christina Heine, Sebastian Bader, Albert Hein, S. Teipel, T. Kirste

引用次数: 9

Using change point detection to automate daily activity segmentation 使用变化点检测自动化日常活动分割

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2017-03-13 DOI: 10.1109/PERCOMW.2017.7917569

S. Aminikhanghahi, D. Cook

引用次数: 29

EmoBGM: Estimating sound's emotion for creating slideshows with suitable BGM EmoBGM:估计声音的情感，用合适的BGM创建幻灯片

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2017-03-13 DOI: 10.1109/PERCOMW.2017.7917587

Cedric Konan, H. Suwa, Yutaka Arakawa, K. Yasumoto

引用次数: 0

Blockchain for IoT security and privacy: The case study of a smart home 物联网安全和隐私的区块链:智能家居的案例研究

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2017-03-13 DOI: 10.1109/PERCOMW.2017.7917634

A. Dorri, S. Kanhere, R. Jurdak, Praveen Gauravaram

{"title":"Blockchain for IoT security and privacy: The case study of a smart home","authors":"A. Dorri, S. Kanhere, R. Jurdak, Praveen Gauravaram","doi":"10.1109/PERCOMW.2017.7917634","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917634","url":null,"abstract":"Internet of Things (IoT) security and privacy remain a major challenge, mainly due to the massive scale and distributed nature of IoT networks. Blockchain-based approaches provide decentralized security and privacy, yet they involve significant energy, delay, and computational overhead that is not suitable for most resource-constrained IoT devices. In our previous work, we presented a lightweight instantiation of a BC particularly geared for use in IoT by eliminating the Proof of Work (POW) and the concept of coins. Our approach was exemplified in a smart home setting and consists of three main tiers namely: cloud storage, overlay, and smart home. In this paper we delve deeper and outline the various core components and functions of the smart home tier. Each smart home is equipped with an always online, high resource device, known as “miner” that is responsible for handling all communication within and external to the home. The miner also preserves a private and secure BC, used for controlling and auditing communications. We show that our proposed BC-based smart home framework is secure by thoroughly analysing its security with respect to the fundamental security goals of confidentiality, integrity, and availability. Finally, we present simulation results to highlight that the overheads (in terms of traffic, processing time and energy consumption) introduced by our approach are insignificant relative to its security and privacy gains.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131466820","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: 1251

Cost-aware virtual machine allocation for off-grid green data centers 离网绿色数据中心的成本意识虚拟机分配

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2017-03-13 DOI: 10.1109/PERCOMW.2017.7917574

Tingting Zhu, Hai Wang, Haikun Wei

引用次数: 3

Detecting spontaneous collaboration in dynamic group activities from noisy individual activity data 从嘈杂的个人活动数据中发现动态群体活动中的自发协作

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2017-03-13 DOI: 10.1109/PERCOMW.2017.7917572

Agnes Grünerbl, G. Bahle, P. Lukowicz

{"title":"Detecting spontaneous collaboration in dynamic group activities from noisy individual activity data","authors":"Agnes Grünerbl, G. Bahle, P. Lukowicz","doi":"10.1109/PERCOMW.2017.7917572","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917572","url":null,"abstract":"This paper investigates the problem of recognizing activities and dynamic ad-hoc collaboration involving multiple users. Thus, we consider people performing various predominantly physical, compound activities in a smart environment (which includes personal/wearable devices). In this case, being “compound” means that the activity can be decomposed into primitive (atomic) actions that are executed by individual users. We investigate how noisy recognition of the atomic actions of individual users can be used to identify instances of cooperation at the level of the compound activities. To this end, we first introduce a hierarchical tree plan library model for activity representation. Using this new model we developed an algorithm, which allows detecting of ad-hoc team interaction without any further knowledge about roles or preliminary designed tasks. We evaluate the model and algorithm “post-mortem” with data extracted from video footage of a real nurse-emergency-training session and with increasing difficulties by artificially adding recognition-errors.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128651346","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

Detecting physical collaborations in a group task using body-worn microphones and accelerometers 使用穿戴式麦克风和加速度计检测小组任务中的物理协作

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2017-03-13 DOI: 10.1109/PERCOMW.2017.7917570

Jamie A. Ward, Gerald Pirkl, Peter Hevesi, P. Lukowicz

{"title":"Detecting physical collaborations in a group task using body-worn microphones and accelerometers","authors":"Jamie A. Ward, Gerald Pirkl, Peter Hevesi, P. Lukowicz","doi":"10.1109/PERCOMW.2017.7917570","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917570","url":null,"abstract":"This paper presents a method of using wearable accelerometers and microphones to detect instances of ad-hoc physical collaborations between members of a group. 4 people are instructed to construct a large video wall and must cooperate to complete the task. The task is loosely structured with minimal outside assistance to better reflect the ad-hoc nature of many real world construction scenarios. Audio data, recorded from chest-worn microphones, is used to reveal information on collocation, i.e. whether or not participants are near one another. Movement data, recorded using 3-axis accelerometers worn on each person's head and wrists, is used to provide information on correlated movements, such as when participants help one another to lift a heavy object. Collocation and correlated movement information is then combined to determine who is working together at any given time. The work shows how data from commonly available sensors can be combined across multiple people using a simple, low power algorithm to detect a range of physical collaborations.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134549617","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: 7

SwallowNet: Recurrent neural network detects and characterizes eating patterns 吞咽网:循环神经网络检测和特征的饮食模式

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2017-03-13 DOI: 10.1109/PERCOMW.2017.7917596

Dzung T. Nguyen, Eli Cohen, M. Pourhomayoun, N. Alshurafa

{"title":"SwallowNet: Recurrent neural network detects and characterizes eating patterns","authors":"Dzung T. Nguyen, Eli Cohen, M. Pourhomayoun, N. Alshurafa","doi":"10.1109/PERCOMW.2017.7917596","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917596","url":null,"abstract":"Passively detecting and counting the number of swallows in food intake enables accurate detection of eating episodes in free-living participants, and aids in characterizing eating episodes. On average, the more food consumed, the greater the number of swallows; and swallows have been shown to positively correlate with caloric intake. While passive sensing measures have shown promise in recent years, they are yet to be used reliably to detect eating, impeding the development of timely intervention delivery that change poor eating behavior. This paper presents a novel integrated wearable necklace that comprises two piezoelectric sensors vertically positioned around the neck, an inertial motion unit, and long short-term memory (LSTM) neural networks to detect and count swallows. A unique correlation of derivative features creates candidate swallows. To reduce the FPR features are extracted using symmetric and asymmetric windows surrounding each candidate swallow to feed into a Random Forest classifier. Independently, a LSTM network is trained from raw data using automated feature learning methods. In an in-lab study comprising confounding activities of 10 participants, results show a 3.34 RMSE of swallow count using LSTM, and a 76.07% average F-measure of swallows, outperforming the Random Forest classifier. This system thus shows promise in accurately detecting and characterizing eating patterns, enabling passive detection of swallow count, and paving the way for timely interventions to prevent problematic eating.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129361330","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: 17