Towards wearable piezoelectric energy harvesting

Tuncel, Yiğit; Bandyopadhyay, Shiva; Kulshrestha, Shambhavi V.; Mendez, Audrey; Ogras, Ümit Y.

doi:10.1145/3370748.3406578

Cited by 19 publications

(7 citation statements)

References 18 publications

(35 reference statements)

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“…On the other hand, walking with specific frequency is not necessary, and any motion that involves knee-bending can be used to gain energy. Another work demonstrated by Tuncel et al built a theoretical model, and performed experiments to assess the amount of energy their harvester scavenged from human body joint motions [ 113 ]. The authors applied two kinds of macro-fiber composite patches and performed 16 groups of experiment to validate their theoretical model.…”

Section: Power Supply Solutions For Wearable Sensorsmentioning

confidence: 99%

Energy Solutions for Wearable Sensors: A Review

Rong

Zheng

Sawan

2021

Sensors

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Wearable sensors have gained popularity over the years since they offer constant and real-time physiological information about the human body. Wearable sensors have been applied in a variety of ways in clinical settings to monitor health conditions. These technologies require energy sources to carry out their projected functionalities. In this paper, we review the main energy sources used to power wearable sensors. These energy sources include batteries, solar cells, biofuel cells, supercapacitors, thermoelectric generators, piezoelectric and triboelectric generators, and radio frequency (RF) energy harvesters. Additionally, we discuss wireless power transfer and some hybrids of the above technologies. The advantages and drawbacks of each technology are considered along with the system components and attributes that make these devices function effectively. The objective of this review is to inform researchers about the latest developments in this field and present future research opportunities.

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Section: Power Supply Solutions For Wearable Sensorsmentioning

confidence: 99%

Energy Solutions for Wearable Sensors: A Review

Rong

Zheng

Sawan

2021

Sensors

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“…Studies have shown that ambient light has the highest potential for EH with power levels of >1 mW and <100 µW in outdoor and indoor conditions, respectively. Similarly, piezoelectric human motion EH can yield about 15 µW [16], while body heat and RF EH have lower power levels of <5 µW, respectively [13].…”

Section: Related Workmentioning

confidence: 99%

“…We use joint bending as the source of mechanical energy, which is enabled by advances in flexible piezoelectric transducers, such as Polyvinylidene Fluoride (PVDF) and Macro-Fiber Composite (MFC). A recent study analyzes piezoelectric EH from joint bending motion and shows that 7.8 µJ per step can be harvested from the knee with an MFC8528P2 piezoelectric element during walking [16]. We use this knowledge to compute the harvested motion energy from the user's activity information.…”

Section: Motion Energy Harvesting Modelmentioning

confidence: 99%

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ECO: Enabling Energy-Neutral IoT Devices through Runtime Allocation of Harvested Energy

Tuncel¹,

Bhat²,

Park³

et al. 2021

Preprint

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Energy harvesting offers an attractive and promising mechanism to power low-energy devices. However, it alone is insufficient to enable an energy-neutral operation, which can eliminate tedious battery charging and replacement requirements. Achieving an energy-neutral operation is challenging since the uncertainties in harvested energy undermine the quality of service requirements. To address this challenge, we present a rollout-based runtime energy-allocation framework that optimizes the utility of the target device under energy constraints. The proposed framework uses an efficient iterative algorithm to compute initial energy allocations at the beginning of a day. The initial allocations are then corrected at every interval to compensate for the deviations from the expected energy harvesting pattern. We evaluate this framework using solar and motion energy harvesting modalities and American Time Use Survey data from 4772 different users. Compared to state-of-theart techniques, the proposed framework achieves 34.6% higher utility even under energy-limited scenarios. Moreover, measurements on a wearable device prototype show that the proposed framework has less than 0.1% energy overhead compared to iterative approaches with a negligible loss in utility.

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“…the battery lifetime and requires frequent recharging, deteriorating the user experience. To mitigate this effect, energy harvesting (EH) from ambient sources, such as light, motion, electromagnetic waves, and body heat, has emerged as a promising solution to power these devices [12,15].…”

mentioning

confidence: 99%

tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices

Başaklar¹,

Tuncel²,

Ogras³

2022

Preprint

Self Cite

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Advances in low-power electronics and machine learning techniques lead to many novel wearable IoT devices. These devices have limited battery capacity and computational power. Thus, energy harvesting from ambient sources is a promising solution to power these low-energy wearable devices. They need to manage the harvested energy optimally to achieve energy-neutral operation, which eliminates recharging requirements. Optimal energy management is a challenging task due to the dynamic nature of the harvested energy and the battery energy constraints of the target device. To address this challenge, we present a reinforcement learning based energy management framework, tinyMAN, for resource-constrained wearable IoT devices. The framework maximizes the utilization of the target device under dynamic energy harvesting patterns and battery constraints. Moreover, tinyMAN does not rely on forecasts of the harvested energy which makes it a prediction-free approach. We deployed tinyMAN on a wearable device prototype using TensorFlow Lite for Micro thanks to its small memory footprint of less than 100 KB. Our evaluations show that tinyMAN achieves less than 2.36 ms and 27.75 μJ while maintaining up to 45% higher utility compared to prior approaches.

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Towards wearable piezoelectric energy harvesting

Cited by 19 publications

References 18 publications

Energy Solutions for Wearable Sensors: A Review

Energy Solutions for Wearable Sensors: A Review

ECO: Enabling Energy-Neutral IoT Devices through Runtime Allocation of Harvested Energy

tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices

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