Energy Efficient Contextual Sensing for Elderly Care

Bhatia, Dinesh; Estevez, Luis; Rao, Sira

doi:10.1109/iembs.2007.4353223

Cited by 19 publications

(6 citation statements)

References 5 publications

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“…The lifetime extension of battery-operated nodes is a crucial aspect of AAL systems, as seniors cannot be reasonably expected to frequently check the residual battery life or replace batteries [ 40 ]. Along this line, the following two subsections present practical guidelines in addressing energy management issues on the microcontroller platforms and the energy-hungry communication (XBee) modules, respectively, to allow deploying extremely energy-efficient systems with dependable battery life.…”

Section: Implementation Issuesmentioning

confidence: 99%

Hands-On Experiences in Deploying Cost-Effective Ambient-Assisted Living Systems

Dasios

Gavalas

Pantziou

et al. 2015

Sensors

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Older adults’ preferences to remain independent in their own homes along with the high costs of nursing home care have motivated the development of Ambient Assisted Living (AAL) technologies which aim at improving the safety, health conditions and wellness of the elderly. This paper reports hands-on experiences in designing, implementing and operating UbiCare, an AAL based prototype system for elderly home care monitoring. The monitoring is based on the recording of environmental parameters like temperature and light intensity as well as micro-level incidents which allows one to infer daily activities like moving, sitting, sleeping, usage of electrical appliances and plumbing components. The prototype is built upon inexpensive, off-the-shelf hardware (e.g., various sensors, Arduino microcontrollers, ZigBee-compatible wireless communication modules) and license-free software, thereby ensuring low system deployment costs. The network comprises nodes placed in a house’s main rooms or mounted on furniture, one wearable node, one actuator node and a centralized processing element (coordinator). Upon detecting significant deviations from the ordinary activity patterns of individuals and/or sudden falls, the system issues automated alarms which may be forwarded to authorized caregivers via a variety of communication channels. Furthermore, measured environmental parameters and activity incidents may be monitored through standard web interfaces.

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Section: Implementation Issuesmentioning

confidence: 99%

Hands-On Experiences in Deploying Cost-Effective Ambient-Assisted Living Systems

Dasios

Gavalas

Pantziou

et al. 2015

Sensors

View full text Add to dashboard Cite

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“…The stringent power requirement and short battery life of wearable devices, however, prevent them from having enough processing power to continuously perform RNN or other kinds of machine learning locally. Moreover, the high energy cost of wirelessly transmitting data, limits the amount of raw sensor data that can be sent and processed externally ( 1 , 2 ). These limitations reduce the accuracy and applicability of machine learning models, especially when data are sampled at very high rates, which leads to latencies ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

Simulation for a Mems-Based CTRNN Ultra-Low Power Implementation of Human Activity Recognition

Emad-ud-din

Hasan

Jafari

et al. 2021

Front. Digit. Health

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This paper presents an energy-efficient classification framework that performs human activity recognition (HAR). Typically, HAR classification tasks require a computational platform that includes a processor and memory along with sensors and their interfaces, all of which consume significant power. The presented framework employs microelectromechanical systems (MEMS) based Continuous Time Recurrent Neural Network (CTRNN) to perform HAR tasks very efficiently. In a real physical implementation, we show that the MEMS-CTRNN nodes can perform computing while consuming power on a nano-watts scale compared to the micro-watts state-of-the-art hardware. We also confirm that this huge power reduction doesn't come at the expense of reduced performance by evaluating its accuracy to classify the highly cited human activity recognition dataset (HAPT). Our simulation results show that the HAR framework that consists of a training module, and a network of MEMS-based CTRNN nodes, provides HAR classification accuracy for the HAPT that is comparable to traditional CTRNN and other Recurrent Neural Network (RNN) implantations. For example, we show that the MEMS-based CTRNN model average accuracy for the worst-case scenario of not using pre-processing techniques, such as quantization, to classify 5 different activities is 77.94% compared to 78.48% using the traditional CTRNN.

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“…In facts, while remote control and monitoring is one of the main advantages of MWSN healthcare systems, energy efficient sensors are often critical [11], [12]. On the other hand communication interfaces such as wifi and bluetooth, while mandatory components of communicating networks, fail to provide support for energy efficient systems [13]. Due to their nature such sensors must be powered by means of electrical batteries, on the other hand their operation cycle is limited due to the unavoidable power exhaustion during time.…”

Section: Introductionmentioning

confidence: 99%

An Entropy Evaluation Algorithm to Improve Transmission Efficiency of Compressed Data in Pervasive Healthcare Mobile Sensor Networks

et al. 2020

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Data transmission is the most critical operation for mobile sensors networks in term of energy waste. Particularly in pervasive healthcare sensors network it is paramount to preserve the quality of service also by means of energy saving policies. Communication and data transmission are among the most critical operation for such devises in term of energy waste. In this paper we present a novel approach to increase battery lifespan by means of shorter transmission due to data compression. On the other hand, since this latter operation has a non-neglectable energy cost, we developed a compression efficiency estimator based on the evaluation of the absolute and relative entropy. Such algorithm provides us with a fast mean for the evaluation of data compressibility. Since mobile wireless sensor networks are prone to battery discharge-related problems, such an evaluation can be used to improve the electrical efficiency of data communication. In facts the developed technique, due to its independence from the string or file length, is extremely robust both for small and big data files, as well as to evaluate whether or not to compress data before transmission. Since the proposed solution provides a quantitative analysis of the source's entropy and the related statistics, it has been implemented as a preprocessing step before transmission. A dynamic threshold defines whether or not to invoke a compression subroutine. Such a subroutine should be expected to greatly reduce the transmission length. On the other hand a data compression algorithm should be used only when the energy gain of the reduced transmission time is presumably greater than the energy used to run the compression software. In this paper we developed an automatic evaluation system in order to optimize the data transmission in mobile sensor networks, by compressing data only when this action is presumed to be energetically efficient. We tested the proposed algorithm by using the Canterbury Corpus as well as standard pictorial data as benchmark test. The implemented system has been proven to be time-inexpensive with respect to a compression algorithm. Finally the computational complexity of the proposed approach is virtually neglectable with respect to the compression and transmission routines themselves.

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Energy Efficient Contextual Sensing for Elderly Care

Cited by 19 publications

References 5 publications

Hands-On Experiences in Deploying Cost-Effective Ambient-Assisted Living Systems

Hands-On Experiences in Deploying Cost-Effective Ambient-Assisted Living Systems

Simulation for a Mems-Based CTRNN Ultra-Low Power Implementation of Human Activity Recognition

An Entropy Evaluation Algorithm to Improve Transmission Efficiency of Compressed Data in Pervasive Healthcare Mobile Sensor Networks

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