Brain-machine interfaces for assistive smart homes- A feasibility study with wearable near-infrared spectroscopy
Published in IEEE-EMBS 2015, 2015
Recommended citation: Takeshi Ogawa, Jun-Ichiro Hirayama, Pankaj Gupta, Hiroki Moriya, Shumpei Yamaguchi, Akihiro Ishikawa, Yoshihiro Inoue, Motoaki Kawanabe, Shin Ishii https://pubmed.ncbi.nlm.nih.gov/26736459/
Smart houses for elderly or physically challenged people need a method to understand residents' intentions during their daily-living behaviors. To explore a new possibility, we here developed a novel brain-machine interface (BMI) system integrated with an experimental smart house, based on a prototype of a wearable near-infrared spectroscopy (NIRS) device, and verified the system in a specific task of controlling of the house's equipments with BMI. We recorded NIRS signals of three participants during typical daily-living actions (DLAs), and classified them by linear support vector machine. In our off-line analysis, four DLAs were classified at about 70% mean accuracy, significantly above the chance level of 25%, in every participant. In an online demonstration in the real smart house, one participant successfully controlled three target appliances by BMI at 81.3% accuracy. Thus we successfully demonstrated the feasibility of using NIRS-BMI in real smart houses, which will possibly enhance new assistive smart-home technologies.
Recommended citation: Takeshi Ogawa, Jun-Ichiro Hirayama, Pankaj Gupta, Hiroki Moriya, Shumpei Yamaguchi, Akihiro Ishikawa, Yoshihiro Inoue, Motoaki Kawanabe, Shin Ishii