Measurement of stressful postures during daily activities: An observational study with older people.

This study measured the postures of older people during cooking and laundry. A sample of men and women aged 75+ years (n=27) was recruited and observed in a home-like environment. Postures were recorded with a measurement system in an objective and detailed manner. The participants were videotaped to be able to see where 'critical' postures occurred, as defined by a trunk inclination of ≥60°. Analysis of data was facilitated by specially developed software. Critical postures accounted for 3% of cooking and 10% of laundry, occurring primarily during retrieving from and putting in lower cabinets, the refrigerator, laundry basket or washing machine as well as disposing into the waste bin. These tasks involve a great variation in postural changes and pose a particular risk to older people. The results suggest that the use of stressful postures may decrease efficiency and increase fatigue, eventually leading to difficulties with daily activities. The specific tasks identified during which critical postures occurred should be targeted by designers in order to improve the activities. A few examples are given of how better design can reduce or eliminate some of the postural constraints.

Optimal control predicts human performance on objects with internal degrees of freedom.

On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework of optimal feedback control to make predictions of how humans should interact with such objects. We confirm the predictions experimentally in a two-dimensional object manipulation task, in which subjects learned to control six different objects with complex dynamics. We show that the non-intuitive behavior observed when controlling objects with internal degrees of freedom can be accounted for by a simple cost function representing a trade-off between effort and accuracy. In addition to using a simple linear, point-mass optimal control model, we also used an optimal control model, which considers the non-linear dynamics of the human arm. We find that the more realistic optimal control model captures aspects of the data that cannot be accounted for by the linear model or other previous theories of motor control. The results suggest that our everyday interactions with objects can be understood by optimality principles and advocate the use of more realistic optimal control models for the study of human motor neuroscience.