EnTrans: Leveraging Kinetic Energy Harvesting Signal for Transportation Mode Detection

Lan, Guohao; Xu, Weitao; Ma, Dong; Khalifa, Sara; Hassan, Mahbub; Hu, Wen

doi:10.1109/tits.2019.2918642

Cited by 25 publications

(16 citation statements)

References 59 publications

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“…network signal information, sensor data, location information, and environmental information like kinetic energy. Among these data sources, network signal is not informative enough to give a fine distinction and kinetic energy is rarely collected by smartphones, though Kinetic energy harvesting (KEH)-based methods [6], [7] are more energy-efficient and simpler.…”

Section: A Relevant Workmentioning

confidence: 99%

Intelligent Scene Recognition Based on Deep Learning

et al. 2021

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Using sensor-rich smartphones to sense various contexts attracts much attention, such as transportation mode recognition. Local solutions make efforts to achieve trade-offs among detection accuracy, delay, and battery usage. We propose a real-time recognition model consisting of two long short-term memory classifiers with different sequence lengths. The shorter one is a binary classifier distinguishing elevator scene and the longer one implements a finer classification among bus, subway, high-speed railway, and others. Light-weighted sensors are employed with a much smaller sampling rate (10Hz) compared with previous works. A two-stage setting makes it robust to scenes with different duration and therefore reduces the latency of recognition greatly. Further, the real-time system refines the classification results and attains smoothed predictions. We present experiments on accuracy and resource usage and prove that our system realizes a latency-low and power-efficient scene recognition approach by trading off a reasonable performance loss (averaged recall of 92.22%).

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Section: A Relevant Workmentioning

confidence: 99%

Intelligent Scene Recognition Based on Deep Learning

et al. 2021

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“…To solve this problem, the literature [7] proposes the concept of intermediate-level features, which represent behavioral features through a set of action attributes learned from the training dataset, which is referred to as an intermediate concept in the paper. The literature [8] uses motion phrases and motion atoms to represent the features of actions in videos. For high-level feature representation, the literature [9] uses an ordering function to model the evolution of motion over time.…”

Section: Related Workmentioning

confidence: 99%

Optimization of a Wireless Sensor-Based Tennis Motion Pattern Recognition System

Ruan

Zhang

2022

Journal of Sensors

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With the rapid development of information technology in today’s era, the application of the Internet, big data, and smart bracelet information technology in the field of sports has enhanced the intelligence of sports and plays an important role in promoting sports performance. This paper focuses on the application of wireless sensors in the field of tennis, using research methods such as literature research, video analysis, comparative research, and mathematical statistics, to explore and analyze the application of wireless sensors in the field of tennis big data, tennis robotics, and the implementation of tennis teaching and training, to provide a theoretical basis for promoting the application of wireless sensors in the field of tennis and also for the broader application of wireless sensors in sports to provide a theoretical reference. For the problem of multiple scales of motion targets in action videos, two video action recognition methods based on high- and low-level feature fusion are proposed, which are the video action recognition methods based on top-down feature fusion and the video action recognition methods based on bottom-up feature fusion. The multipowered mobile anchor nodes are allowed to move along a prescribed route and broadcast multiple power signals, and then, the location of the unknown node is estimated using a four-ball intersection weight center-of-mass algorithm. Simulations show experimentally that the algorithm reduces the average localization error and requires fewer anchor nodes.

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“…Previous works employ the open circuit AC voltage [11], [13], [14] from the energy harvester or capacitor voltage [15] for extracting context information. There are various sensing points in the energy harvesting circuit that offer two types of sensing signals i.e., voltage and current which contain context information.…”

Section: Exploring Multiple Sensing Pointsmentioning

confidence: 99%

Towards Energy Positive Sensing using Kinetic Energy Harvesters

Sandhu¹,

Geissdoerfer²,

Khalifa³

et al. 2020

Preprint

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Conventional systems for motion context detection rely on batteries to provide the energy required for sampling a motion sensor. Batteries, however, have limited capacity and, once depleted, have to be replaced or recharged. Kinetic Energy Harvesting (KEH) allows to convert ambient motion and vibration into usable electricity and can enable batteryless, maintenance free operation of motion sensors. The signal from a KEH transducer correlates with the underlying motion and may thus directly be used for context detection, saving space, cost and energy by omitting the accelerometer. Previous work uses the open circuit or the capacitor voltage for sensing without using the harvested energy to power a load. In this paper, we propose to use other sensing points in the KEH circuit that offer informationrich sensing signals while the energy from the harvester is used to power a load. We systematically analyze multiple sensing signals available in different KEH architectures and compare their performance in a transport mode detection case study. To this end, we develop four hardware prototypes, conduct an extensive measurement campaign and use the data to train and evaluate different classifiers. We show that sensing the harvesting current signal from a transducer can be energy positive, delivering up to ten times as much power as it consumes for signal acquisition, while offering comparable detection accuracy to the accelerometer signal for most of the considered transport modes.

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EnTrans: Leveraging Kinetic Energy Harvesting Signal for Transportation Mode Detection

Cited by 25 publications

References 59 publications

Intelligent Scene Recognition Based on Deep Learning

Intelligent Scene Recognition Based on Deep Learning

Optimization of a Wireless Sensor-Based Tennis Motion Pattern Recognition System

Towards Energy Positive Sensing using Kinetic Energy Harvesters

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