Energy allocation for activity recognition in wearable devices with kinetic energy harvesting

Abstract

Harvesting kinetic energy from body movement is regarded as a promising rechargeable energy source for wearable devices with low‐power. Energy allocation is essential for motion‐based rechargeable devices since the great variability of energy gained from movement. Based on the realistic characteristics of an ultra‐low‐power wearable devices and our measurement observations, we propose the optimization framework allocating energy to maximize the average accuracy of human activity recognition and provide an offline and online algorithm, respectively. We evaluate the proposed energy allocation approach with real‐world human activity and kinetic energy harvesting datasets. Experimental results validate that our proposed energy allocation approach can maximize the energy allocation utility and improve energy efficiency of wearable devices.

Software and hardware co‐design for sustainable cyber‐physical systems

“…To overcome the shortcomings of the common electric vehicle charging model, the article 4 proposes an innovative charge method to improve electric vehicles users' charging experience and a cooperative control‐based power scheduling algorithm to increase energy efficiency. Xiao et al 5 studied the energy allocation problem for activity recognition in wearable devices with kinetic energy harvesting. They developed a hybrid optimization framework that maximizes the average accuracy of human activity recognition through energy allocation.…”

Self‐Powered Smart Shoes with Functional Ribbon Units for Monitoring Human Gait

“…[ 1–3 ] To overcome this limitation, research on the conversion of the kinetic energy of the human body into electrical energy and the utilization of harvested electrical energy has gained popularity. [ 4–8 ] Many energy harvesting mechanisms, such as triboelectric nanogenerators (TENGs), [ 9–11 ] piezoelectric generators (PEGs), [ 12–14 ] and thermoelectric generators (TEGs), [ 15–17 ] use human body motion to generate electric power; however, the generated electric power is too low to be directly stored in ordinary rechargeable batteries. For low‐power storage, capacitors are an alternative; however, they have fast discharging characteristics, which may restrict the usage of the stored electric power.…”

A computational approach for progressive architecture shrinkage in action recognition

“…10 Although recently there has been a growing interest in efficient video processing, and architectural modification have been investigated in literature, 8,11 to the best of our knowledge this is the first paper to employ a sequential shrinkage approach to reduce the computational complexity of an existing network. While we chose to focus on action recognition 12 because of its elevated computational requirements, we notice that our approach is general and could in principle be applied to any deep neural network. Because of the significant reduction in computational load it can generate, the proposed approach could benefit several other application scenarios in which computational resources are limited or real-time processing is mandatory, like Edge AI 13 or medical imaging.…”