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"... Abstract—Body area networks are used extensively in the medical field and elderly care. These networks perform a collection of roles including monitoring an individual’s activities, through a process known as activity recognition. Current approaches to activity recognition require specialized hardwa ..."
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Abstract—Body area networks are used extensively in the medical field and elderly care. These networks perform a collection of roles including monitoring an individual’s activities, through a process known as activity recognition. Current approaches to activity recognition require specialized hardware for advanced sensors which puts stress on battery life. We show that it is theoretically possible to distinguish between different human activities/postures by using radio signal propagation only and provide strategies for doing this. This is immensely beneficial to the field of sensor networks for two reasons: 1) It removes the need for more energy intensive components thus reducing strain on limited power resources and thus 2) reduces form factor for sensor network nodes. We show that activity recognition can be done using only radio signals at low transmit power levels with good accuracy. Lower power consumption and reduced form factor are desirable features for body networks and have been identified as being essential for wide adoption of body networks. I.
1 A Review on Radio Based Activity Recognition
"... Recognizing human activities in their daily living enables the development and widely usage of human-centric applications, such as health monitoring, assisted living, etc. Traditional activity recognition methods often rely on physical sensors (camera, accelerometer, gyroscope, etc.) to continuously ..."
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Recognizing human activities in their daily living enables the development and widely usage of human-centric applications, such as health monitoring, assisted living, etc. Traditional activity recognition methods often rely on physical sensors (camera, accelerometer, gyroscope, etc.) to continuously collect sensor readings, and utilize pattern recognition algorithms to identify user's activities at an aggregator. Though traditional activity recognition methods have been demonstrated to be effective in previous work, they raise some concerns such as privacy, energy consumption and deployment cost. In recent years, a new activity recognition approach, which takes advantage of body attenuation and/or channel fading of wireless radio, has been proposed. Compared with traditional activity recognition methods, radio based methods utilize wireless transceivers in environments as infrastructure, exploit radio communication characters to achieve high recognition accuracy, reduce energy cost and preserve user's privacy. In this paper, we divide radio based methods into four categories: ZigBee radio based activity recognition, WiFi radio based activity recognition, RFID radio based activity recognition, and other radio based activity recognition. Some existing work in each category is introduced and reviewed in detail. Then, we compare some representative methods to show their advantages and disadvantages. At last, we point out some future research directions of this new research topic. 2 1.
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"... Abstract—Body area networks are used extensively in the medical field and elderly care. These networks perform a collection of roles including monitoring an individual’s activities, through a process known as activity recognition. Current approaches to activity recognition require specialized hardwa ..."
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Abstract—Body area networks are used extensively in the medical field and elderly care. These networks perform a collection of roles including monitoring an individual’s activities, through a process known as activity recognition. Current approaches to activity recognition require specialized hardware for advanced sensors which puts stress on battery life. We show that it is theoretically possible to distinguish between different human activities/postures by using radio signal propagation only and provide strategies for doing this. This is immensely beneficial to the field of sensor networks for two reasons: 1) It removes the need for more energy intensive components thus reducing strain on limited power resources and thus 2) reduces form factor for sensor network nodes. We show that activity recognition can be done using only radio signals at low transmit power levels with high accuracy. Lower power consumption and reduced form factor are desirable features for body networks and have been identified as being essential for wide adoption of body networks. I.
1Exploiting the Data Sensitivity of Neurometric Fidelity for Optimizing EEG Sensing
"... Abstract—With newly developed wireless neuroheadsets, elec-troencephalography (EEG) neurometrics can be incorporated into in-situ and ubiquitous physiological monitoring for hu-man mental health. As a resource constraint system providing critical health services, the EEG headset design must consider ..."
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Abstract—With newly developed wireless neuroheadsets, elec-troencephalography (EEG) neurometrics can be incorporated into in-situ and ubiquitous physiological monitoring for hu-man mental health. As a resource constraint system providing critical health services, the EEG headset design must consider both high application fidelity and energy efficiency. However, through empirical studies with an off-the-shelf Emotiv EPOC Neuroheadset, we uncover a mismatch between lossy EEG sensor communication and high neurometric application fidelity requirements. To tackle this problem, we study how to learn the sensitivity of neurometric application fidelity to EEG data. The learned sensitivity is used to develop two algorithms: an energy minimization algorithm minimizing the energy usage in EEG sampling and networking while meeting applications ’ fidelity requirements; a fidelity maximization algorithm maximizing the sum of all applications ’ fidelities through the incorporation and optimal utilization of a limited data buffer. The effectiveness of our proposed solutions is validated through trace-driven experiments. Index Terms—EEG sensors, neurometric fidelity, energy effi-ciency, data sensitivity analysis.
AdaSense: Adapting Sampling Rates for Activity Recognition in Body Sensor Networks
"... Abstract—In a Body Sensor Network (BSN) activity recognition system, sensor sampling and communication quickly deplete battery reserves. While reducing sampling and communication saves energy, this energy savings usually comes at the cost of reduced recognition accuracy. To address this challenge, w ..."
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Abstract—In a Body Sensor Network (BSN) activity recognition system, sensor sampling and communication quickly deplete battery reserves. While reducing sampling and communication saves energy, this energy savings usually comes at the cost of reduced recognition accuracy. To address this challenge, we propose AdaSense, a framework that reduces the BSN sensors sampling rate while meeting a user-specified accuracy requirement. AdaSense utilizes a classifier set to do either multi-activity classification that requires a high sampling rate or single activity event detection that demands a very low sampling rate. AdaSense aims to utilize lower power single activity event detection most of the time. It only resorts to higher power multi-activity classification to find out the new activity when it is confident that the activity changes. Furthermore, AdaSense is able to determine the optimal sampling rates using a novel Genetic Programming algorithm. Through this Genetic Programming approach, AdaSense reduces sampling rates for both lower power single activity event detection and higher power multi-activity classification. With an existing BSN dataset and a smartphone dataset we collect from eight subjects, we demonstrate that AdaSense effectively reduces BSN sensors sampling rate and outperforms a state-of-the-art solution in terms of energy savings.
