Developing a novel, cost-effective and location-independent approach to investigate upper limb kinematics: Remote monitoring of an unrestricted reaching task via smartphone application.

Though ideally motor control experiments should be conducted in a controlled environment, such as that found in the laboratory, there is a strong need to develop remote monitoring methods for populations with reduced mobility, allowing for examination of subjects in natural environments and conditions. Furthermore, due to the current pandemic, this necessity appears to be even more urgent. As a proof of concept, we present a novel approach to investigate reaching kinematics remotely. We took advantage of the fact that 72% percent of the smartphone-owners use an Android operative system [1] and of the availability of a free data-logging app (i.e., Androsensor), which allows gathering data from the smartphones’ sensors, including tri-axial accelerometers, to reconstruct the devices’ movement kinematics. We then combined this technological opportunity with the design of a simple and unrestricted reaching task, in which subjects were asked to perform upper-limb movements either in the ipsilateral or in the contralateral individual visual field. We tested this approach on healthy male and female volunteers (n=25, age=27.25±5.4 yrs), monitoring the upper limbs movements during our reaching task while on one-to-one videoconferences. Following installation of the app on their smartphone, participants were instructed to execute the reaching movements for 20 consecutive times while holding the smartphone in their hand. The experiment was performed in 8 conditions in a randomized order, in particular ipsilateral and contralateral reaching movements were executed separately by both dominant and non dominant hand, as well as with eyes open and closed. In order to obtain an acceleration signal baseline for each device, the dataset from every subject included an additional recording in which the smartphone was placed on the desk for about 5 seconds. Pre-processing techniques were employed to transform the raw yxz accelerations to earth-centered inertial accelerations and to attenuate signal noise. From the processed linear acceleration signals we computed other kinematics parameters including the movement distance, the maximal speed achieved, and movement smoothness. Currently, we are performing statistical analysis of the obtained movement kinematics across conditions. Preliminary results seem to be encouraging in terms of discriminating between experimental conditions. The present approach for remote monitoring of reaching movements could provide the great advantage of being cost-effective, time-efficient and scalable which is a step toward a location-independent investigation of motor control.