Automatic task analysis based on head movement

Analysis of movement using accelerometers mounted on the torso or limbs has shown good potential for the recognition of physical activities. However many contemporary lifestyle tasks are sedentary, and less is known about how these can be automatically characterized using movement signals. This paper proposes possibly the first system that employs head movement for recognizing different levels of mental activity and for discriminating between various kinds of sedentary and non-sedentary tasks. Results from analysis of a 20-participant database show that head movement is surprisingly indicative of cognitive load and discriminative between different task types, as well as exhibiting some sensitivity to the instant of task change. Given the possibility for wearing hats or glasses with embedded inertial measurement units, this suggests a range of interesting future applications, including monitoring of sedentary daily activities, and developing rough estimates of mental exertion.