Fraunhofer Center for Assistive Information and Communication Solutions – AICOSDescription:
People living with Parkinson’s are followed in neurology consultations every six months. During these consultations, neurologists perform the neurological examination and interview patients about their issues and needs. Based on the insights from the examination and interview, neurologists suggest adjustments to the medication plan.
However, the examination and interview are often inconclusive. Neurologists can never be sure of the usual severity of the symptoms, since neurologists only have sporadic encounters with patients. Neurologists are never sure about the times when people experience off-phases of the medication, and, thus, cannot address them completely with medication changes. Moreover, neurologists can only detect significant changes in symptoms, provided they do not have tools for objectively tracking symptoms of the condition between consultations.
There is a lack of information in the medical encounter regarding medication effects in between medical appointments. Deciding medication or DBS adjustments is complex, because neurological examination is episodic, short, and distanced. Our vision is to enrich medical appointments with data from the patient’s everyday life, retrieved from the motions sensors of the smartphone. The continuous and automatic extraction of motion metrics could provide valuable information for a complementary analysis for axial symptoms from the activities of daily living, that include walking speed, step length, stride asymmetry and activity periods characterization.
FhP has developed a continuous and automatic gait analysis smartphone application - that allows the extraction of movement related metrics as: gait asymmetry, gait speed, time-to-sit and to stand, physical activity detection and characterization and detection of falls. The data is analysed by the application in different periods during the day, which allow a continuous and non obstructive characterization of activities. However, the accuracy of our movement algorithms was carried out with young and elderly participants without Parkinson’s disease. Thus, there is a need for assessment of these algorithms with patients with PD, to account for possible adaptations and further validation in controlled and uncontrolled settings. The intention is to adapt a continuous gait monitoring system for people with Parkinson’s.
Author: João Simões
Type: MSc thesis
Partner: Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa
Year: 2019