Litcius/Paper detail

Wearable Intelligent Human–Machine Interfaces Ready for Sustainable Edge Computing Systems

Minglu Zhu, Shuhan He, Tao Chen, Chengkuo Lee

2025AI Sensors6 citationsDOIOpen Access PDF

Abstract

To better serve human life with smart and harmonic communication between the real and digital worlds, wearable human–machine interfaces (HMIs) with edge computing capabilities indicate the path to the next revolution of information technology. In this review, we focus on wearable HMIs and highlight several key aspects which are worth investigating. Firstly, we review wearable HMIs powered by commercial-ready technologies, highlighting some limitations. Next, to establish a dual-way interaction for exchanging comprehensive information, sensing and feedback functions on the human body need to be customized based on specific scenarios. Power consumption is another primary issue that is critical to wearable applications due to limited space, one that is possible to be solved by energy harvesting techniques and self-powered data transmission approaches. To further improve the data interpretation with higher intelligence, machine learning (ML)-assisted analysis is preferred for multi-dimensional data. Eventually, with the presence of edge computing systems, those data can be pre-processed locally for downstream applications. Generally, this review offers an overview of the development of intelligent wearable HMIs with edge computing capabilities and self-sustainability, which can greatly enhance the user experience in healthcare, industrial productivity, education, etc.

Topics & Concepts

Wearable computerComputer scienceEdge computingWearable technologyEnhanced Data Rates for GSM EvolutionHuman–computer interactionKey (lock)Focus (optics)Cloud computingIntelligent sensorEmbedded systemEnergy consumptionPhysical computingUser interfaceUbiquitous computingIntelligent decision support systemBig dataEdge deviceData transmissionEngineeringAdvanced Sensor and Energy Harvesting MaterialsInnovative Energy Harvesting TechnologiesContext-Aware Activity Recognition Systems