Power reduction by varying sampling rate

Dieter, W.R.; Datta, S.; Kai, Wong Key

doi:10.1109/lpe.2005.195519

Cited by 7 publications

(9 citation statements)

References 18 publications

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“…Moreover, the algorithms were validated while downsampling the signals and still have shown an excellent performance. It has been reported that lower sampling frequency allows to scale down CPU speed, thus, reducing the power consumption [34]. As shown in Table 2, our study has performed at lowest sampling frequency among studies that used accelerometers or IMU sensors.…”

Section: Discussionmentioning

confidence: 96%

An Energy-Efficient Algorithm for Classification of Fall Types Using a Wearable Sensor

et al. 2019

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Objective: To mitigate damage from falls, it is essential to provide medical attention expeditiously. Many previous studies have focused on detecting falls and have shown that falls can be accurately detected at least in a laboratory setting. However, a very few studies have classified the different types of falls. To this end, in this paper, a novel energy-efficient algorithm that can discriminate the five most common fall types was developed for wearable systems. Methods: A wearable system with an inertial measurement unit sensor was first developed. Then, our novel algorithm, temporal signal angle measurement (TSAM), was used to classify the different types of falls at various sampling frequencies, and the results were compared with those from three different machine learning algorithms. Results: The overall performance of the TSAM and that of the machine learning algorithms were similar. However, the TSAM outperformed the machine learning algorithms at frequencies in the range of 10-20 Hz. As the sampling frequency dropped from 200 to 10Hz, the accuracy of the TSAM ranged from 93.3% to 91.8%. The sensitivity and specificity ranges from 93.3% to 91.8%, and 98.3% to 97.9%, respectively for the same frequency range. Conclusion: Our algorithm can be utilized with energy-efficient wearable devices at low sampling frequencies to classify different types of falls. Significance: Our system can expedite medical assistance in emergency situations caused by falls by providing the necessary information to medical doctors or clinicians. INDEX TERMS Fall detection, fall type classification, machine learning, temporal signal angle measurement, wearable device.

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Section: Discussionmentioning

confidence: 96%

An Energy-Efficient Algorithm for Classification of Fall Types Using a Wearable Sensor

et al. 2019

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“…(41) According to Theorems 3 and 4, the MDL mechanism is strategyproof. Note that the objective function in (19) is convex with respect to L M = (x M , y M ). Hence, for each data sample (x, y), we can efficiently compute the optimal solution L * M = (x * M , y * M ) to minimize the SC platform's crowdsourcing cost in (19) without considering strategyproofness and then use it as the label.…”

Section: B Deep Learning Based Mobile Bs Deployment Mechanismmentioning

confidence: 99%

“…Note that the objective function in (19) is convex with respect to L M = (x M , y M ). Hence, for each data sample (x, y), we can efficiently compute the optimal solution L * M = (x * M , y * M ) to minimize the SC platform's crowdsourcing cost in (19) without considering strategyproofness and then use it as the label. In the training process, we adopt the mean squared error (MSE) to evaluate the training loss and optimize the deep neural network parameters.…”

Section: B Deep Learning Based Mobile Bs Deployment Mechanismmentioning

confidence: 99%

Mechanism Design for Wireless Powered Spatial Crowdsourcing Networks

Jiao

Wang

Niyato

et al. 2020

IEEE Trans. Veh. Technol.

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Wireless power transfer (WPT) is a promising technology to prolong the lifetime of the sensors and communication devices, i.e., workers, in completing crowdsourcing tasks by providing continuous and cost-effective energy supplies. In this paper, we propose a wireless powered spatial crowdsourcing framework which consists of two mutually dependent phases: task allocation phase and data crowdsourcing phase. In the task allocation phase, we propose a Stackelberg game based mechanism for the spatial crowdsourcing platform to efficiently allocate spatial tasks and wireless charging power to each worker. In the data crowdsourcing phase, the workers may have an incentive to misreport its real working location to improve its utility, which causes adverse effects to the spatial crowdsourcing platform. To address this issue, we present three strategyproof deployment mechanisms for the spatial crowdsourcing platform to place a mobile base station, e.g., vehicle or robot, which is responsible for transferring the wireless power and collecting the crowdsourced data. As the benchmark, we first apply the classical median mechanism and evaluate its worst-case performance. Then, we design a conventional strategyproof deployment mechanism to improve the expected utility of the spatial crowdsourcing platform under the condition that the workers' locations follow a known geographical distribution. For a more general case with only the historical location data available, we propose a deep learning based strategyproof deployment mechanism to maximize the spatial crowdsourcing platform's utility. Extensive experimental results based on synthetic and real-world datasets reveal the effectiveness of the proposed framework in allocating tasks and charging power to workers while avoiding the dishonest worker's manipulation.

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“…However, although halving the sampling frequency (clock rate) of a transistor cuts its power consumption in half (e.g. Dieter et al 2005), it may also decrease the quality recorded physiological signal, thereby adding a source of error during HRV outcome measure calculation.…”

Section: Implications For the Hrv Literaturementioning

confidence: 99%

A careful look at ECG sampling frequency and R-peak interpolation on short-term measures of heart rate variability

Ellis¹,

Zhu²,

Koenig³

et al. 2015

Physiol. Meas.

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As the literature on heart rate variability (HRV) continues to burgeon, so too do the challenges faced with comparing results across studies conducted under different recording conditions and analysis options. Two important methodological considerations are (1) what sampling frequency (SF) to use when digitizing the electrocardiogram (ECG), and (2) whether to interpolate an ECG to enhance the accuracy of R-peak detection. Although specific recommendations have been offered on both points, the evidence used to support them can be seen to possess a number of methodological limitations. The present study takes a new and careful look at how SF influences 24 widely used time- and frequency-domain measures of HRV through the use of a Monte Carlo-based analysis of false positive rates (FPRs) associated with two-sample tests on independent sets of healthy subjects. HRV values from the first sample were calculated at 1000 Hz, and HRV values from the second sample were calculated at progressively lower SFs (and either with or without R-peak interpolation). When R-peak interpolation was applied prior to HRV calculation, FPRs for all HRV measures remained very close to 0.05 (i.e. the theoretically expected value), even when the second sample had an SF well below 100 Hz. Without R-peak interpolation, all HRV measures held their expected FPR down to 125 Hz (and far lower, in the case of some measures). These results provide concrete insights into the statistical validity of comparing datasets obtained at (potentially) very different SFs; comparisons which are particularly relevant for the domains of meta-analysis and mobile health.

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Power reduction by varying sampling rate

Cited by 7 publications

References 18 publications

An Energy-Efficient Algorithm for Classification of Fall Types Using a Wearable Sensor

An Energy-Efficient Algorithm for Classification of Fall Types Using a Wearable Sensor

Mechanism Design for Wireless Powered Spatial Crowdsourcing Networks

A careful look at ECG sampling frequency and R-peak interpolation on short-term measures of heart rate variability

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