HARKE: Human Activity Recognition from Kinetic Energy Harvesting Data in Wearable Devices

Khalifa, Sara; Lan, Guohao; Hassan, Mahbub; Seneviratne, Aruna; Das, Sajal K.

doi:10.1109/tmc.2017.2761744

Cited by 132 publications

(90 citation statements)

References 47 publications

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“…H. Kalantarian et al have used a piezo element as an insole pressure sensor and a power source [11]. Also, S. Khalifa et al have used it as a motion sensor and a power source [13]. However, in all of these research studies, including our glove, electricity is not generated unless it is in force or in motion.…”

Section: B Energy Harvesting Elementsmentioning

confidence: 99%

EHAAS: Energy Harvesters As A Sensor for Place Recognition on Wearables

Umetsu

Nakamura

Arakawa

et al. 2019

2019 IEEE International Conference on Pervasive Computing and Communications (PerCom

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A wearable based long-term lifelogging system is desirable for the purpose of reviewing and improving users lifestyle habits. Energy harvesting (EH) is a promising means for realizing sustainable lifelogging. However, present EH technologies suffer from instability of the generated electricity caused by changes of environment, e.g., the output of a solar cell varies based on its material, light intensity, and light wavelength. In this paper, we leverage this instability of EH technologies for other purposes, in addition to its use as an energy source. Specifically, we propose to determine the variation of generated electricity as a sensor for recognizing "places" where the user visits, which is important information in the lifelogging system. First, we investigate the amount of generated electricity of selected energy harvesting elements in various environments. Second, we design a system called EHAAS (Energy Harvesters As A Sensor) where energy harvesting elements are used as a sensor. With EHAAS, we propose a place recognition method based on machine-learning and implement a prototype wearable system. Our prototype evaluation confirms that EHAAS achieves a place recognition accuracy of 88.5% F-value for nine different indoor and outdoor places. This result is better than the results of existing sensors (3-axis accelerometer and brightness). We also clarify that only two types of solar cells are required for recognizing a place with 86.2% accuracy.

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Section: B Energy Harvesting Elementsmentioning

confidence: 99%

EHAAS: Energy Harvesters As A Sensor for Place Recognition on Wearables

Umetsu

Nakamura

Arakawa

et al. 2019

2019 IEEE International Conference on Pervasive Computing and Communications (PerCom

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“…Khalifa et al [68] conducted further experiments to assess (a) how well KEH can detect human activities when worn on other body parts, such as wrist, instead of in the foot 2 , (b) how accurately KEH can detect activities compared to accelerometers, and (c) how much power can be saved if KEH, which does not require a power supply, is used for HAR instead of an accelerometer. They also used piezoelectric KEH and conducted experiments with ten subjects performing five different activities, standing, walking, running, ascending stairs, and descending stairs, while holding the KEH device in the hand.…”

Section: A Context Sensing From Kinetic Ehmentioning

confidence: 99%

Sensing, Computing, and Communications for Energy Harvesting IoTs: A Survey

Lan

Hassan

et al. 2020

IEEE Commun. Surv. Tutorials

Self Cite

238

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Sensing Optimizations Computing Optimizations Communications Optimizations  Commercial products and services  Standards for wireless protocol and transducer test method  Checkpointing optimization  Timekeeping across power failures  Packet-less communication  Reinforcement learning based communication optimization  Backscatter communication  Kinetic EH sensing  Thermal EH sensing  Solar EH sensing  RF EH sensingTABLE I: Vendors specializing in energy harvesting components and solutions suitable for IoTs. Company Source Energy Harvester EH Solution Application Foundation Year Piezo Systems Kinetic × piezoelectric energy harvesting 1988 MIDE Technology Kinetic × piezoelectric energy harvesting

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“…An interesting fact for energy harvesting powered IoT devices is that the physical contexts the device is monitoring are usually related to the energy harvesting source of the harvester [10]. For instance, for kinetic-powered wearable IoTs, Khalifa et al [11] have investigated the use of the AC voltage signal generated by KEH for wearable sensing. The underpin of the idea is that the kinetic energy transducer can be modeled as an inertial oscillating system when it operates in the inertialforce mode.…”

Section: B Kinetic Energy Harvesting Based Context Sensingmentioning

confidence: 99%

EnTrans: Leveraging Kinetic Energy Harvesting Signal for Transportation Mode Detection

Lan

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, as well as providing location-based services. In existing systems, accelerometer and GPS are the dominantly used signal sources for transportation context monitoring which drain out the limited battery life of the wearable devices very quickly. To resolve the high energy consumption issue, in this paper, we present EnTrans, which enables transportation mode detection by using only the kinetic energy harvester as an energy-efficient signal source. The proposed idea is based on the intuition that the vibrations experienced by the passenger during traveling with different transportation modes are distinctive. Thus, voltage signal generated by the energy harvesting devices should contain sufficient features to distinguish different transportation modes. We evaluate our system using over 28 hours of data, which is collected by eight individuals using a practical energy harvesting prototype. The evaluation results demonstrate that EnTrans is able to achieve an overall accuracy over 92% in classifying five different modes while saving more than 34% of the system power compared to conventional accelerometer-based approaches.

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HARKE: Human Activity Recognition from Kinetic Energy Harvesting Data in Wearable Devices

Cited by 132 publications

References 47 publications

EHAAS: Energy Harvesters As A Sensor for Place Recognition on Wearables

EHAAS: Energy Harvesters As A Sensor for Place Recognition on Wearables

Sensing, Computing, and Communications for Energy Harvesting IoTs: A Survey

EnTrans: Leveraging Kinetic Energy Harvesting Signal for Transportation Mode Detection

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