To incorporate emotional cues into how people interact with machines, Zhang and his team use cameras and sensors to capture information on blood pressure, heartbeat, skin conductivity (think sweaty palms) and eye movement. For example, rolling the eyes could signify fatigue or exasperation, while a wandering gaze might indicate boredom.
A computer to predict human emotions then analyzes data. Zhang said the system can accurately predict a person's emotional state about 90 per cent of the time.
The work has many potential applications, including physical rehabilitation. One machine in Zhang's lab has the patient hold the end of a mechanical arm attached to a computer. The person manipulates the mechanical arm to move objects on a computer screen, mimicking wrist rehabilitation exercises. Sensors track patient performance and software infers when they are getting frustrated or fatigued.
"If we can understand the emotional state of the patient, we can know this state may significantly disturb the functional performance," Zhang said.
The next step is to create passive and active feedback systems. Zhang uses the analogy of gym equipment: an exercise bike is passive, in that the user must decide to pedal, while a treadmill is active - the user must keep moving. The team wants to take this one step further.
"We want both physical and mind," Zhang said. "This is the novel aspect of our approach."
For example, as the wrist rehabilitation system monitored emotions, it could cue messages of encouragement. It could also prevent patients from overdoing it if it senses they are pushing themselves too hard.
At home, the system would become a virtual partner. That is, the computer would learn from the patient and help them direct their own rehabilitation.
"My plan is not only management of patient function and performance, but also that emotions become active in rehabilitation. We would have on screen an advisor - like a friend."
"This whole project is based on the concept of home-based rehabilitation," Zhang said. "That is very important for Saskatchewan, where many people live far away from cities and major hospital facilities and they prefer to stay at home."
Zhang's work is funded by the Natural Sciences and Engineering Research Council.
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University of Saskatchewan