This paper surveys the current research directions of activity recognition using inertial sensors, with potential application in healthcare, wellbeing and sports The analysis of related work is organized according to the five main steps involved in the activity recognition process: preprocessing, segmentation, feature extraction, dimensionality reduction and classification For each step, we present the main techniques utilized, their advantages and drawbacks, performance metrics and usage examples We also discuss the research challenges, such as user behavior and technical limitations, as well as the remaining open research questions

https://www.pervasive.jku.at/Teaching/_2011SS/EmbeddedSystems/Uebungen/UE09/2010_Activity%20Recognition%20Using%20Inertial%20Sensing%20for%20Healthcare_Avci.pdf

Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey

Purpose: To obtain insight into what kind of monitoring technologies exist to monitor activity in-home, what the characteristics and aims of applying these technologies are, what kind of research has been conducted on their effects and what kind of outcomes are reported. Methods: A systematic document search was conducted within the scientific databases Pubmed, Embase, Cochrane, PsycINFO and Cinahl, complemented by Google Scholar. Documents were included in this review if they reported on monitoring technologies that detect activities of daily living (ADL) or significant events, e.g. falls, of elderly people in-home, with the aim of prolonging independent living. Results: Five main types of monitoring technologies were identified: PIR motion sensors, body-worn sensors, pressure sensors, video monitoring and sound recognition. In addition, multicomponent technologies and smart home technologies were identified. Research into the use of monitoring technologies is widespread, but in its infancy, cons...

Literature review on monitoring technologies and their outcomes in independently living elderly people.

A medical sensor network can wirelessly monitor vital signs of humans, making it useful for long-term health care without sacrificing patient comfort and mobility. For such a network to be viable, its design must protect data privacy and authenticity given that medical data are highly sensitive. We identify the unique security challenges of such a sensor network and propose a set of resource-efficient mechanisms to address these challenges. Our solution includes (1) a novel two-tier scheme for verifying the authenticity of patient data, (2) a secure key agreement protocol to set up shared keys between sensor nodes and base stations, and (3) symmetric encryption/decryption for protecting data confidentiality and integrity. We have implemented the proposed mechanisms on a wireless mote platform, and our results confirm their feasibility.

/pdf/design-and-implementation-of-a-securewireless-mote-based-1fnpxlfjax.pdf

Design and Implementation of a SecureWireless Mote-Based Medical Sensor Network

This paper addresses two key technological barriers to the wider adoption of patient telemonitoring systems for chronic disease management, namely, usability and sensor device interoperability. As a great percentage of chronic patients are elderly patients as well, usability of the system has to be adapted to their needs. This paper identifies (from previous research) a set of design criteria to address these challenges, and describes the resulting system based on a wireless sensor network, and including a node as a custom-made interface that follows usability design criteria stated. This system has been tested with 22 users (mean age 65) and evaluated with a validated usability questionnaire. Results are good and improve those of other systems based on TV or smartphone. Our results suggest that user interfaces alternative to TVs and smartphones could play an important role on the usability of sensor networks for patient monitoring. Regarding interoperability, only very recently a standard has been published (2010, the ISO IEEE 11073 Personal health devices) that can support the needs of limited computational power environments typical of patient monitoring sensor networks.

Usability and Interoperability in Wireless Sensor Networks for Patient Telemonitoring in Chronic Disease Management

Parkinson disease (PD) is a slow destructive disorder of the central nervous system in which dopamine, i.e., catecholamine neurotransmitter in the central nervous system is lost. PD hurts patients’ movement and speech ability. Sometimes, it can also affect patients’ mood, behavior, and thinking ability. Falling down is a common problem in PD patients and on time fall detection is important to assist PD patients and prevent them from being injured. To this end, being able to correctly distinguish various activities, e.g. walking, sitting, standing still, is a must. To monitor activities and moving patterns of PD patients, a wireless body sensor network (BSN) may prove to be useful. By attaching various wireless sensor nodes on the body of PD patients or integrating them into their shoes or cloths, their activities and physiological conditions can be checked regularly and an alarm can be generated in case of emergency or need for additional assistances. A wireless body sensor network consists of a number of wireless sensor nodes that cooperatively monitor physical (e.g. motion) and physiological (e.g. heart rate) conditions of a person. In addition to sensors, each sensor node is typically equipped with a radio transceiver or other wireless communication devices, a small microcontroller as processing unit, and an energy source in a form of a battery. Sensor nodes may vary in size and type of sensors they are equipped with. Size and cost constraints on sensor nodes cause limitations on their resources in terms of energy, memory, and computational processing. Figure 1 shows an example of a body sensor network. Previous studies for activity recognition of PD patients mostly use accelerometer and occasionally gyroscope sensors attached to various parts of patients’ body (JJ., HA. et al. 1991; Aminian, Robert et al. 1999; JI., AA. et al. 2001; JI., V. et al. 2004; N., T. et al. 2004; White, Wagenaar et al. 2006; Moorea, MacDougalla et al. 2007; Salarian, Russmann et al. 2007). One of the main criticisms on the previous studies is that they use centralized techniques which not only require expensive equipments to monitor physiological conditions and activities of patients [e.g. Vitaport 3 (White, Wagenaar et al. 2006)] but also introduce delays in the detection process. Also due to having a single point of failure they are more prone to failures and crashes. In contrary, we propose a fusion-based distributed algorithm which can be easily implemented on resource constrained wireless sensor nodes and detect and distinguish activities in (near) real-time. Our approach offers three main advantages: (i) distributed processing and reasoning which decreases the data processing

/pdf/sensor-fusion-based-activity-recognition-for-parkinson-1ijjilrepq.pdf

Sensor Fusion-Based Activity Recognition for Parkinson Patients

Advances in networking, sensors, and embedded devices have made it feasible to monitor and provide medical and other assistance to people in their homes. Aging populations will benefit from reduced costs and improved healthcare through assisted living based on these technologies. However, these systems challenge current state-of-the-art techniques for usability, reliability, and security. In this paper we present the PAS open architecture for assisted living, which allows independently developed third party components to collaborate. We discuss key technological issues in assisted living systems, such as tracking, fall detection, security and privacy; and results from our pilot study in a real assisted living facility are presented.

/pdf/pas-a-wireless-enabled-sensor-integrated-personal-assistance-4uvagmz8gg.pdf

PAS: A Wireless-Enabled, Sensor-Integrated Personal Assistance System for Independent and Assisted Living

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PAS: A Wireless-Enabled, Sensor-Integrated Personal Assistance System for Independent and Assisted Living