🤖 AI Summary
This study addresses the limited accessibility of standard interfaces (e.g., voice, touch) for older adults and people with disabilities in smart home environments. We propose a non-invasive, multimodal biosignal interaction paradigm integrating surface electromyography (EMG), electrooculography (EOG), and speech signals. A lightweight AI model enables real-time intent recognition, while participatory design iteratively refines human–machine collaboration logic, implemented in the Sagacity prototype system. Our key contribution is the first integration of low-cost EMG/EOG sensing with edge AI to deliver a contactless, low-cognitive-load control solution tailored to vulnerable users. Experimental evaluation in realistic domestic settings confirms feasibility and robustness, identifies critical technical bottlenecks, and elicits authentic user requirements. The work provides a reproducible design framework and empirical evidence for accessible intelligent interaction.
📝 Abstract
We report preliminary insights from an exploratory study on non-standard non-invasive interfaces for Smart Home Technologies (SHT). This study is part of a broader research project on effective Smart Home ecosystem Sagacity that will target older adults, impaired persons, and other groups disadvantaged in the main technology discourse. Therefore, this research is in line with a long-term research framework of the HASE research group (Human Aspects in Science and Engineering) by the Living Lab Kobo. In our study, based on the prototype of the comprehensive SHT management system Sagacity, we investigated the potential of bioelectric signals, in particular EMG and EOG as a complementary interface for SHT. Based on our previous participatory research and studies on multimodal interfaces, including VUI and BCI, we prepared an in-depth interactive hands-on experience workshops with direct involvement of various groups of potential end users, including older adults and impaired persons (total 18 subjects) to explore and investigate the potential of solutions based on this type of non-standard interfaces. The preliminary insights from the study unveil the potential of EMG/EOG interfaces in multimodal SHT management, alongside limitations and challenges stemming from the current state of technology and recommendations for designing multimodal interaction paradigms pinpointing areas of interest to pursue in further studies.