Stereoscopic visual stimuli for examining biological motion perception and unanticipated steering manoeuvres in people with Parkinson's disease

Abstract

Community falls in people with Parkinson's disease (PwPD) are common, costly, and often unanticipated. Aside from static obstacles, it has been reported that oncoming people in community settings pose problems for PwPD when navigating. This suggests that PwPD may have difficulty (i) perceiving biological motion and action possibilities, and (ii) steering out of the way of oncoming persons. To date, laboratory research that investigated unanticipated steering manoeuvres in PwPD have only incorporated light- or arrow-based visual stimuli to simulate the spatiotemporal demands of these movements. However, such simple stimuli are not ecologically valid for examining biological motion perception and unanticipated steering manoeuvres used in avoiding oncoming people. To improve the generalisability of laboratory research in this field, a set of stereoscopic visual stimuli that feature an oncoming person initiating a sudden change in direction was developed for PwPD to engage with. Specifically, we modified and improved existing cinematographic techniques, software, and stereoscopic display technology to bring about: Ambulatory scenarios that were quasi-immersed with the laboratory environment. Enhanced realism. Better temporal consistency in video playback.

Feasibility of virtual low-cost quantitative continuous measurement of movements in the extremities of people with Parkinson’s disease

“…This protocol will provide the bases for telemedicine throughout the world. Utilization of state-of-the-art wearable sensors [ 10 , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] ] will enhance future investigations.…”

“…We are developing this protocol with groundbreaking related techniques [ 10 , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [26] , [27] , 29 , 30 , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] ]. In particular, we are exploring ways to utilize computers and other technologies to construct methods to efficiently and effectively measure movements in clinical settings [ 10 , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [26] , [27] , 29 , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] ], generate signals and transforms to generate signatures of Parkinson's disease and related conditions [ [7] , [8] , [9] , [10] , [11] , …”