How Much Energy Can We Harvest Daily for Wearable Applications?

Tuncel, Yiğit; Başaklar, Toygun; Ogras, Ümit Y.

doi:10.1109/islped52811.2021.9502507

Cited by 8 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this work, we used a logarithmic utility function with a diminishing rate of return, as elaborated in Section 4.1. EH Source: The EH source uses the dataset presented in [16] to generate EH scenarios according to different user patterns. This dataset uses the combination of light and motion energy as the ambient energy sources, and it combines power measurement data with the activity and location information of 4772 users from the American Time Use Survey dataset [18] to generate varying 24hour EH patterns per user.…”

Section: Problem Formulationmentioning

confidence: 99%

“…This dataset uses the combination of light and motion energy as the ambient energy sources, and it combines power measurement data with the activity and location information of 4772 users from the American Time Use Survey dataset [18] to generate varying 24hour EH patterns per user. We divide the EH dataset [16] into four clusters according to the users' EH patterns throughout the day. The hourly distributions of these four clusters are illustrated in Figure 2.…”

Section: Problem Formulationmentioning

confidence: 99%

“…These characteristics do not change over time during the training. The EH dataset [16] is divided into four clusters according to the users' EH patterns throughout the day. The agent is trained separately on each cluster.…”

Section: Proposed Rl Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

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

Başaklar¹,

Tuncel²,

Ogras³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

See 1 more Smart Citation

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

Başaklar¹,

Tuncel²,

Ogras³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The ring sensors include four small-sized (3 mm by 3 mm) silver electrodes to achieve non-invasive bioimpedance sensing. In addition, once research level investigation is finalized the rings can easily host the necessary hardware (a microcontroller-MCU chip with integrated analog-to-digital converter-ADC for bioimpedance signal sampling and digital-to-analog converter-DAC for programmable signal injection to skin, possibly a Bluetooth module for data transfer) and smart power management 19 to provide a fully wearable bioimpedance operation.…”

Section: Introductionmentioning

confidence: 99%

Continuous cuffless blood pressure monitoring with a wearable ring bioimpedance device

et al. 2023

View full text Add to dashboard Cite

Smart rings provide unique opportunities for continuous physiological measurement. They are easy to wear, provide little burden in comparison to other smart wearables, are suitable for nocturnal settings, and can be sized to provide ideal contact between the sensors and the skin at all times. Continuous measuring of blood pressure (BP) provides essential diagnostic and prognostic value for cardiovascular health management. However, conventional ambulatory BP measurement devices operate using an inflating cuff that is bulky, intrusive, and impractical for frequent or continuous measurements. We introduce ring-shaped bioimpedance sensors leveraging the deep tissue sensing ability of bioimpedance while introducing no sensitivity to skin tones, unlike optical modalities. We integrate unique human finger finite element model with exhaustive experimental data from participants and derive optimum design parameters for electrode placement and sizes that yields highest sensitivity to arterial volumetric changes, with no discrimination against varying skin tones. BP is constructed using machine learning algorithms. The ring sensors are used to estimate arterial BP showing peak correlations of 0.81, and low error (systolic BP: 0.11 ± 5.27 mmHg, diastolic BP: 0.11 ± 3.87 mmHg) for >2000 data points and wide BP ranges (systolic: 89–213 mmHg and diastolic: 42–122 mmHg), highlighting the significant potential use of bioimpedance ring for accurate and continuous estimation of BP.

show abstract

A Survey of Embedded Machine Learning for Smart and Sustainable Healthcare Applications

An,

Tuncel,

Basaklar

et al. 2023

Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing

View full text Add to dashboard Cite

How Much Energy Can We Harvest Daily for Wearable Applications?

Cited by 8 publications

References 17 publications

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

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

Continuous cuffless blood pressure monitoring with a wearable ring bioimpedance device

A Survey of Embedded Machine Learning for Smart and Sustainable Healthcare Applications

Contact Info

Product

Resources

About