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@inproceedings{YiWillemson13,
	Author = {X. Yi and J. Willemson and F. Nait-Abdesselam},
	Booktitle = {2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications},
	Date-Added = {2017-05-14 07:47:40 +0000},
	Date-Modified = {2017-05-14 07:48:44 +0000},
	Keywords = {biomedical equipment;cryptography;medical computing;wireless sensor networks;Cybernetica;Sharemind system;advanced cryptographic techniques;attribute-based encryption;eavesdropping;input data;lightweight encryption algorithm;low-cost sensor nodes;low-power sensor nodes;open air;patient data privacy;patient database;privacy-preserving wireless medical sensor network;sensor node;spoofing;symmetric key cryptosystems;Communication system security;Cryptography;Medical services;Protocols;Servers;Wireless communication;Wireless sensor networks;Medical sensor network;SHA-3;Sharemind;privacy-preserving computation},
	Month = {July},
	Pages = {118-125},
	Title = {Privacy-Preserving Wireless Medical Sensor Network},
	Year = {2013},
	Bdsk-File-1 = { }}

@inproceedings{JalalKamal14,
	Author = {A. Jalal and S. Kamal and D. Kim},
	Booktitle = {Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT)},
	Date-Added = {2017-05-14 07:42:12 +0000},
	Date-Modified = {2017-05-14 07:43:07 +0000},
	Pages = {1-6},
	Title = {Depth map-based human activity tracking and recognition using body joints features and Self-Organized Map},
	Year = {2014},
	Bdsk-File-1 = { }}

@inproceedings{BodorJackson03,
	Author = {Bodor, Robert and Jackson, Bennett and Papanikolopoulos, Nikolaos},
	Booktitle = {Proc. of the 11th Mediterranean Conf. on Control and Automation},
	Date-Added = {2017-05-14 07:40:29 +0000},
	Date-Modified = {2017-05-14 07:41:33 +0000},
	Title = {Vision-based human tracking and activity recognition},
	Volume = {1},
	Year = {2003},
	Bdsk-File-1 = { }}

@article{Fortino15,
	Abstract = {Abstract Body Sensor Networks (BSNs) have emerged as the most effective technology enabling not only new e-Health methods and systems but also novel applications in human-centered areas such as electronic health care, fitness/welness systems, sport performance monitoring, interactive games, factory workers monitoring, and social physical interaction. Despite their enormous potential, they are currently mostly used only to monitor single individuals. Indeed, \{BSNs\} can proactively interact and collaborate to foster novel \{BSN\} applications centered on collaborative groups of individuals. In this paper, C-SPINE, a framework for Collaborative \{BSNs\} (CBSNs), is proposed. \{CBSNs\} are \{BSNs\} able to collaborate with each other to fulfill a common goal. They can support the development of novel smart wearable systems for cyberphysical pervasive computing environments. Collaboration therefore relies on interaction and synchronization among the \{CBSNs\} and on collaborative distributed computing atop the collaborating CBSNs. Specifically, collaboration is triggered upon \{CBSN\} proximity and relies on service-specific protocols allowing for managing services among the collaborating CBSNs. C-SPINE also natively supports multi-sensor data fusion among \{CBSNs\} to enable joint data analysis such as filtering, time-dependent data integration and classification. To demonstrate its effectiveness, C-SPINE is used to implement e-Shake, a collaborative \{CBSN\} system for the detection of emotions. The system is based on a multi-sensor data fusion schema to perform automatic detection of handshakes between two individuals and capture of possible heart-rate-based emotion reactions due to the individuals' meeting. },
	Author = {Giancarlo Fortino and Stefano Galzarano and Raffaele Gravina and Wenfeng Li},
	Date-Added = {2017-05-14 07:10:48 +0000},
	Date-Modified = {2017-05-14 07:23:10 +0000},
	Doi = {https://doi.org/10.1016/j.inffus.2014.03.005},
	Issn = {1566-2535},
	Journal = {Information Fusion},
	Keywords = {Handshake detection},
	Pages = {50 - 70},
	Title = {A framework for collaborative computing and multi-sensor data fusion in body sensor networks},
	Url = {http://www.sciencedirect.com/science/article/pii/S156625351400044X},
	Volume = {22},
	Year = {2015},
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S156625351400044X},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.inffus.2014.03.005}}

@inproceedings{Bourke16,
	Author = {Bourke, Alan K and Ihlen, Espen AF and Van de Ven, Pepijn and Nelson, John and Helbostad, Jorunn L},
	Booktitle = {Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the},
	Date-Added = {2017-05-13 14:39:15 +0000},
	Date-Modified = {2017-05-14 07:44:51 +0000},
	Organization = {IEEE},
	Pages = {4881--4884},
	Title = {Video analysis validation of a real-time physical activity detection algorithm based on a single waist mounted tri-axial accelerometer sensor},
	Year = {2016},
	Bdsk-File-1 = { }}

@inproceedings{Gheid16,
	Author = {Gheid, Zakaria and Challal, Yacine},
	Booktitle = {13th International Conference on Ubiquitous Intelligence and Computing (UIC 2016)},
	Date-Added = {2017-05-13 14:39:11 +0000},
	Date-Modified = {2017-05-14 07:46:54 +0000},
	Title = {Novel Efficient and Privacy-Preserving Protocols For Sensor-Based Human Activity Recognition},
	Year = {2016},
	Bdsk-File-1 = { }}

@article{LuFu09,
	Author = {Lu, C. H. and Fu, L. C.},
	Date-Added = {2017-05-13 07:27:06 +0000},
	Date-Modified = {2017-05-13 07:28:17 +0000},
	Doi = {10.1109/TASE.2009.2021981},
	Issn = {1545-5955},
	Journal = {IEEE Transactions on Automation Science and Engineering},
	Keywords = {belief networks;home automation;mobile computing;telecommunication network reliability;wireless sensor networks;Bayesian network fusion;ambient intelligence applications;attentive home pilot project;context-aware attentive services;device failure;robust location-aware activity recognition;smart home;wireless sensor network;Location-aware activity recognition;ambient-intelligence compliant object (AICO);attentive home;wireless sensor network},
	Month = {Oct},
	Number = {4},
	Pages = {598-609},
	Title = {Robust Location-Aware Activity Recognition Using Wireless Sensor Network in an Attentive Home},
	Volume = {6},
	Year = {2009},
	Bdsk-File-1 = { },
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/TASE.2009.2021981}}

@article{Yick20082292,
	Abstract = {A wireless sensor network (WSN) has important applications such as remote environmental monitoring and target tracking. This has been enabled by the availability, particularly in recent years, of sensors that are smaller, cheaper, and intelligent. These sensors are equipped with wireless interfaces with which they can communicate with one another to form a network. The design of a \{WSN\} depends significantly on the application, and it must consider factors such as the environment, the application's design objectives, cost, hardware, and system constraints. The goal of our survey is to present a comprehensive review of the recent literature since the publication of [I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor networks, \{IEEE\} Communications Magazine, 2002]. Following a top-down approach, we give an overview of several new applications and then review the literature on various aspects of WSNs. We classify the problems into three different categories: (1) internal platform and underlying operating system, (2) communication protocol stack, and (3) network services, provisioning, and deployment. We review the major development in these three categories and outline new challenges. },
	Author = {Jennifer Yick and Biswanath Mukherjee and Dipak Ghosal},
	Date-Added = {2017-05-13 06:59:55 +0000},
	Date-Modified = {2017-05-13 06:59:55 +0000},
	Doi = {https://doi.org/10.1016/j.comnet.2008.04.002},
	Issn = {1389-1286},
	Journal = {Computer Networks},
	Keywords = {Survey},
	Number = {12},
	Pages = {2292 - 2330},
	Title = {Wireless sensor network survey},
	Url = {http://www.sciencedirect.com/science/article/pii/S1389128608001254},
	Volume = {52},
	Year = {2008},
	Bdsk-File-1 = { },
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S1389128608001254},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.comnet.2008.04.002}}

@article{Milenkovic20062521,
	Abstract = {Recent technological advances in sensors, low-power integrated circuits, and wireless communications have enabled the design of low-cost, miniature, lightweight, and intelligent physiological sensor nodes. These nodes, capable of sensing, processing, and communicating one or more vital signs, can be seamlessly integrated into wireless personal or body networks (WPANs or WBANs) for health monitoring. These networks promise to revolutionize health care by allowing inexpensive, non-invasive, continuous, ambulatory health monitoring with almost real-time updates of medical records via the Internet. Though a number of ongoing research efforts are focusing on various technical, economic, and social issues, many technical hurdles still need to be resolved in order to have flexible, reliable, secure, and power-efficient \{WBANs\} suitable for medical applications. This paper discusses implementation issues and describes the authors' prototype sensor network for health monitoring that utilizes off-the-shelf 802.15.4 compliant network nodes and custom-built motion and heart activity sensors. The paper presents system architecture and hardware and software organization, as well as the authors' solutions for time synchronization, power management, and on-chip signal processing. },
	Author = {Aleksandar Milenkovi{\'c} and Chris Otto and Emil Jovanov},
	Date-Added = {2017-05-13 06:58:37 +0000},
	Date-Modified = {2017-05-13 06:58:37 +0000},
	Doi = {https://doi.org/10.1016/j.comcom.2006.02.011},
	Issn = {0140-3664},
	Journal = {Computer Communications},
	Note = {Wirelsess Senson Networks and Wired/Wireless Internet Communications},
	Number = {13--14},
	Pages = {2521 - 2533},
	Title = {Wireless sensor networks for personal health monitoring: Issues and an implementation},
	Url = {http://www.sciencedirect.com/science/article/pii/S0140366406000508},
	Volume = {29},
	Year = {2006},
	Bdsk-File-1 = { },
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S0140366406000508},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.comcom.2006.02.011}}

@article{Gravina16,
	Abstract = {Abstract This paper proposes Activity as a Service (Activity-aaService), a full-fledged cyber--physical framework to support community, on-line and off-line human activity recognition and monitoring in mobility. Activity-aaService is able to address the current lack of Cloud-Assisted Body Area Networks platforms and applications supporting monitoring and analysis of human activity for single individuals and communities. Activity-aaService is built atop the BodyCloud platform so enabling efficient BSN-based sensor data collection and local processing (Body-side), high performance computing of collected sensor data and data storing on the Cloud (Cloud-side), workflow-based programming of data analysis (Analyst-side), and advanced visualization of results (Viewer-side). Specifically, it provides specific, powerful and flexible programming abstractions for the rapid prototyping of efficient human activity-oriented applications. The effectiveness of the proposed framework has been demonstrated through the development of several prototypes related to physical activity monitoring, step counting, physical energy estimation, automatic fall detection, and smart wheelchair support. Finally, performance evaluation of the proposed framework at the Body-side of the activity classification has been carried out by analyzing processing load, data transmission time, \{CPU\} usage, memory footprint, and battery consumption using four heterogeneous mobile devices representing low, medium and high performance mobile platforms. },
	Author = {Raffaele Gravina and Congcong Ma and Pasquale Pace and Gianluca Aloi and Wilma Russo and Wenfeng Li and Giancarlo Fortino},
	Date-Added = {2017-05-13 03:20:32 +0000},
	Date-Modified = {2017-05-13 06:52:28 +0000},
	Doi = {https://doi.org/10.1016/j.future.2016.09.006},
	Issn = {0167-739X},
	Journal = {Future Generation Computer Systems},
	Keywords = {Software as a service},
	Month = {September},
	Title = {Cloud-based Activity-aaService cyber--physical framework for human activity monitoring in mobility},
	Url = {http://www.sciencedirect.com/science/article/pii/S0167739X16303016},
	Year = {2016},
	Bdsk-File-1 = { },
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S0167739X16303016},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.future.2016.09.006}}

@article{WangWu17,
	Abstract = {Injuries that are caused by falls have been regarded as one of the major health threats to the independent living for the elderly. Conventional fall detection systems have various limitations. In this work, we first look for the correlations between different radio signal variations and activities by analyzing radio propagation model. Based on our observation, we propose WiFall, a truly unobtrusive fall detection system. WiFall employs physical layer Channel State Information (CSI) as the indicator of activities. It can detect fall of the human without hardware modification, extra environmental setup, or any wearable device. We implement WiFall on desktops equipped with commodity 802.11n NIC, and evaluate the performance in three typical indoor scenarios with several layouts of transmitter-receiver (Tx-Rx) links. In our area of interest, WiFall can achieve fall detection for a single person with high accuracy. As demonstrated by the experimental results, WiFall yields 90 percent detection precision with a false alarm rate of 15 percent on average using a one-class SVM classifier in all testing scenarios. It can also achieve average 94 percent fall detection precisions with 13 percent false alarm using Random Forest algorithm.},
	Annote = {http://ieeexplore.ieee.org/abstract/document/7458186/},
	Author = {Y. Wang and K. Wu and L. M. Ni},
	Date-Added = {2017-05-13 03:19:27 +0000},
	Date-Modified = {2017-05-13 06:50:32 +0000},
	Doi = {10.1109/TMC.2016.2557792},
	Issn = {1536-1233},
	Journal = {IEEE Transactions on Mobile Computing},
	Keywords = {biomedical communication;decision trees;geriatrics;indoor radio;medical computing;pattern classification;radio links;radio receivers;radio transmitters;support vector machines;wireless LAN;wireless sensor networks;802.11n NIC;WiFall;device-free fall detection;false alarm rate;health threats;indoor scenarios;one-class SVM classifier;physical layer channel state information;radio propagation model;radio signal;random forest algorithm;transmitter-receiver links;wireless networks;Channel state information;IEEE 802.11 Standard;Motion detection;Senior citizens;Sensors;Wireless communication;Wireless sensor networks;Wireless;channel state information;device-free;fall detection;machine learning},
	Month = {Feb},
	Number = {2},
	Pages = {581-594},
	Title = {WiFall: Device-Free Fall Detection by Wireless Networks},
	Volume = {16},
	Year = {2017},
	Bdsk-File-1 = { },
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/TMC.2016.2557792}}

@article{Prabhu17,
	Abstract = {Engineers have created WSN applications for areas including health care, utilities, and remote monitoring. In health care, wireless devices make less invasive patient monitoring and health care possible. For utilities such as the electricity grid, streetlights, and water municipals, wireless sensors offer a lower-cost method for collecting system health data to reduce energy usage and better manage resources. Remote monitoring covers a wide range of applications where wireless systems can complement wired systems by reducing wiring costs and allowing new types of measurement applications. The most well-liked principle for distributed clustering methodology is to choose cluster heads with more residual energy and to rotate them occasionally. Sensors at very heavy traffic locations rapidly deplete their energy resources and die in advance, before the network to collapse. The use of these sensors and the probability of organizing them into networks have discovered many research issues and have highlighted innovative ways to cope with certain problems. Here, the view of distributed clustering mechanism has been elaborated elegantly and different areas where such distributed clustering methodology could be put to use in emerging real world wireless sensor network applications have been compiled and discussed.},
	Annote = {https://ssrn.com/abstract=2909105},
	Author = {Prabhu, Boselin and Balakumar, N and Antony, A Johnson},
	Date-Added = {2017-05-13 03:15:40 +0000},
	Date-Modified = {2017-05-13 06:50:20 +0000},
	Journal = {INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY},
	Keywords = {Wireless Sensor Network, Sensor Node, Distributed Clustering, Energy Utilization, Real World Applications},
	Month = {Jan.},
	Number = {8},
	Title = {Wireless Sensor Network Based Smart Environment Applications},
	Volume = {3},
	Year = {2017},
	Bdsk-File-1 = { }}

@article{LeeChung09,
	Abstract = {The smart shirt which measures electrocardiogram (ECG) and acceleration signals for continuous and real time health monitoring is designed and developed. The shirt mainly consists of sensors for continuous monitoring the health data and conductive fabrics to get the body signal as electrodes. The measured physiological \{ECG\} data and physical activity data are transmitted in an ad-hoc network in \{IEEE\} 802.15.4 communication standard to a base-station and server \{PC\} for remote monitoring. The wearable sensor devices are designed to fit well into shirt with small size and low power consumption to reduce the battery size. The adaptive filtering method to cancel artifact noise from conductive fabric electrodes in a shirt is also designed and tested to get clear \{ECG\} signal even though during running or physical exercise of a person. },
	Author = {Young-Dong Lee and Wan-Young Chung},
	Date-Added = {2017-05-13 03:14:52 +0000},
	Date-Modified = {2017-05-13 06:53:06 +0000},
	Doi = {https://doi.org/10.1016/j.snb.2009.04.040},
	Issn = {0925-4005},
	Journal = {Sensors and Actuators B: Chemical},
	Month = {July},
	Number = 2,
	Pages = {390 - 395},
	Title = {Wireless sensor network based wearable smart shirt for ubiquitous health and activity monitoring},
	Url = {http://www.sciencedirect.com/science/article/pii/S0925400509003724},
	Volume = 140,
	Year = 2009,
	Bdsk-File-1 = { },
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S0925400509003724}}

@article{AlemdarErsoy10,
	Abstract = {Becoming mature enough to be used for improving the quality of life, wireless sensor network technologies are considered as one of the key research areas in computer science and healthcare application industries. The pervasive healthcare systems provide rich contextual information and alerting mechanisms against odd conditions with continuous monitoring. This minimizes the need for caregivers and helps the chronically ill and elderly to survive an independent life, besides provides quality care for the babies and little children whose both parents have to work. Although having significant benefits, the area has still major challenges which are investigated in this paper. We provide several state of the art examples together with the design considerations like unobtrusiveness, scalability, energy efficiency, security and also provide a comprehensive analysis of the benefits and challenges of these systems. },
	Author = {Hande Alemdar and Cem Ersoy},
	Date-Modified = {2017-05-13 06:52:42 +0000},
	Doi = {https://doi.org/10.1016/j.comnet.2010.05.003},
	Issn = {1389-1286},
	Journal = {Computer Networks},
	Keywords = {Children and chronically ill},
	Month = {October},
	Number = {15},
	Pages = {2688 - 2710},
	Title = {Wireless sensor networks for healthcare: A survey},
	Url = {http://www.sciencedirect.com/science/article/pii/S1389128610001398},
	Volume = {54},
	Year = {2010},
	Bdsk-File-1 = { },
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S1389128610001398},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.comnet.2010.05.003}}
	
@webpage{Zscore,
	Date-Added = {2017-06-12 10:16:21 +0000},
	Date-Modified = {2017-06-12 10:16:44 +0000},
	Lastchecked = {Monday, 12 June 2017},
	Title = {Standard score - Wikipedia},
	Url = {https://en.wikipedia.org/wiki/Standard_score},
	Bdsk-Url-1 = {https://en.wikipedia.org/wiki/Standard_score}
}

