A new multisensor software architecture for movement detection: Preliminary study with people with cerebral palsy. Issue 97 (January 2017)

• Record Type: Journal Article
• Authors: Molina, Alberto; Guerrero, Jaime; Gómez, Isabel; Merino, Manuel
• Is Part Of: International journal of human-computer studies. Issue 97(2017)
• Page Start: 45
• Page End: 57
• Publication Date: 2017-01
• Subjects: Cerebral palsy -- Accelerometer -- Flex sensor -- Switch -- Adaptive classifier; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2016.08.003
• Languages: English
• ISSNs: 1071-5819

Abstract

Abstract: A five-layered software architecture translating movements into mouse clicks has been developed and tested on an Arduino platform with two different sensors: accelerometer and flex sensor. The architecture comprises low-pass and derivative filters, an unsupervised classifier that adapts continuously to the strength of the user's movements and a finite state machine which sets up a timer to prevent involuntary movements from triggering false positives. Four people without disabilities and four people with cerebral palsy (CP) took part in the experiments. People without disabilities obtained an average of 100% and 99.3% in precision and true positive rate (TPR) respectively and there were no statistically significant differences among type of sensors and placement. In the same experiment, people with disabilities obtained 97.9% and 100% in precision and TPR respectively. However, these results worsened when subjects used the system to access a communication board, 89.6% and 94.8% respectively. With their usual method of access-an adapted switch- they obtained a precision and TPR of 86.7% and 97.8% respectively. For 3-out-of-4 participants with disabilities our system detected the movement faster than the switch. For subjects with CP, the accelerometer was the easiest to use because it is more sensitive to gross motor motion than the flex sensor which requires more complex movements. A final survey showed that 3-out-of-4 participants with disabilities would prefer to use this new technology instead of their traditional method of access. Abstract : Highlights: The software architecture translates movements into clicks using accelerometers and flex sensors. It comprises filters and a classifier adapting to the strength of users' movements. Involuntary movements are filtered out by using a finite state machine. Disabled people obtained a 89.6% and 94.8% in precision and TPR respectively. Tests showed that 3 out of 4 disabled people would prefer to use this new technology.