Towards energy efficient sensor nodes for online activity recognition

Grützmacher, Florian; Wolff, Johann-Peter; Hein, Albert; Lepidis, Polichronis; Dorsch, Rainer G.; Kirste, Thomas; Haubelt, Christian

doi:10.1109/iecon.2017.8217455

Cited by 7 publications

(7 citation statements)

References 22 publications

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“…Other researchers found that low-power activity recognition may rely on more effective use of the sensing device. Grutzmacher et al [27] and Elsts et al [28] relegate the feature extraction work to the device rather than the server, which lowers the overall energy consumption because of a decreased need for data transmission. Bhat et al [29] found that they could achieve activity recognition accuracy as high as 97.7% even with a low-power IoT device, and Braojos et al [19] achieved up to 97.2% accuracy with low-power wearable nodes.…”

Section: Related Workmentioning

confidence: 99%

Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection

Culman

Aminikhanghahi

Cook

2020

Sensors

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Continuous monitoring of complex activities is valuable for understanding human behavior and providing activity-aware services. At the same time, recognizing these activities requires both movement and location information that can quickly drain batteries on wearable devices. In this paper, we introduce Change Point-based Activity Monitoring (CPAM), an energy-efficient strategy for recognizing and monitoring a range of simple and complex activities in real time. CPAM employs unsupervised change point detection to detect likely activity transition times. By adapting the sampling rate at each change point, CPAM reduces energy consumption by 74.64% while retaining the activity recognition performance of continuous sampling. We validate our approach using smartwatch data collected and labeled by 66 subjects. Results indicate that change point detection techniques can be effective for reducing the energy footprint of sensor-based mobile applications and that automated activity labels can be used to estimate sensor values between sampling periods.

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Section: Related Workmentioning

confidence: 99%

Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection

Culman

Aminikhanghahi

Cook

2020

Sensors

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“…Other approaches, orthogonal to those aforementioned, study the on-board calculation of the feature extraction stage of activity recognition systems. In [14][15][16][17][18][19], it was shown that calculating features on the wireless sensor nodes could reduce the energy consumption of their wireless transceivers or flash memories due to the reduced amounts of data.…”

Section: Related Workmentioning

confidence: 99%

“…[28][29][30]. Since this stage often drastically reduces the data rate, as a relatively small number of features is calculated from a large number of samples of a window, it was subject to a lot of research to perform this stage as near to the sensor as possible, i.e., on board of a wireless sensor node [14][15][16]18,19], or even on-sensor [17,21]. However, each application-specific setup varies in sensor sampling frequency, sliding window size, sliding window overlap, and number of features and their computational effort in calculating them.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…The component with the highest influence on the wireless sensor node's energy consumption is the wireless link [17]. In [21], we have shown how to build a similar energy model of a BTLE transceiver which is directly dependent on the number of BTLE packets sent in a specified time interval, i.e., with a certain frequency.…”

Section: Energy Models Of Hardware Componentsmentioning

confidence: 99%

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Model-Based Design of Energy-Efficient Human Activity Recognition Systems with Wearable Sensors

et al. 2018

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The advances in MEMS technology development allow for small and thus unobtrusive designs of wearable sensor platforms for human activity recognition. Multiple such sensors attached to the human body for gathering, processing, and transmitting sensor data connected to platforms for classification form a heterogeneous distributed cyber-physical system (CPS). Several processing steps are necessary to perform human activity recognition, which have to be mapped to the distributed computing platform. However, the software mapping is decisive for the CPS’s processing load and communication effort. Thus, the mapping influences the energy consumption of the CPS, and its energy-efficient design is crucial to prolong battery lifetimes and allow long-term usage of the system. As a consequence, there is a demand for system-level energy estimation methods in order to substantiate design decisions even in early design stages. In this article, we propose to combine well-known dataflow-based modeling and analysis techniques with energy models of wearable sensor devices, in order to estimate energy consumption of wireless sensor nodes for online activity recognition at design time. Our experiments show that a reasonable system-level average accuracy above 97% can be achieved by our proposed approach.

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“…Furthermore, in activity recognition settings with wearable sensor nodes, data collected at sampling frequencies of e.g., 100 Hz has either to be stored for longer periods of time or transmitted via wireless interfaces. The motivation to reduce sensor data already on the wearable sensor node results from the significant energy consumption introduced by either transmitting raw sensor data wirelessly [ 5 , 8 ] or by storing it to flash memory for later offline processing [ 9 ]. Both consumes a significant amount of energy on wearable sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

Time and Memory Efficient Online Piecewise Linear Approximation of Sensor Signals

Grützmacher

Beichler

Hein

et al. 2018

Sensors

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Piecewise linear approximation of sensor signals is a well-known technique in the fields of Data Mining and Activity Recognition. In this context, several algorithms have been developed, some of them with the purpose to be performed on resource constrained microcontroller architectures of wireless sensor nodes. While microcontrollers are usually constrained in computational power and memory resources, all state-of-the-art piecewise linear approximation techniques either need to buffer sensor data or have an execution time depending on the segment’s length. In the paper at hand, we propose a novel piecewise linear approximation algorithm, with a constant computational complexity as well as a constant memory complexity. Our proposed algorithm’s worst-case execution time is one to three orders of magnitude smaller and its average execution time is three to seventy times smaller compared to the state-of-the-art Piecewise Linear Approximation (PLA) algorithms in our experiments. In our evaluations, we show that our algorithm is time and memory efficient without sacrificing the approximation quality compared to other state-of-the-art piecewise linear approximation techniques, while providing a maximum error guarantee per segment, a small parameter space of only one parameter, and a maximum latency of one sample period plus its worst-case execution time.

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Towards energy efficient sensor nodes for online activity recognition

Cited by 7 publications

References 22 publications

Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection

Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection

Model-Based Design of Energy-Efficient Human Activity Recognition Systems with Wearable Sensors

Time and Memory Efficient Online Piecewise Linear Approximation of Sensor Signals

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