Gait Parameters Measured from Wearable Sensors Reliably Detect Freezing of Gait in a Stepping in Place Task

Abstract

Freezing of gait (FOG), a debilitating symptom of Parkinson’s disease (PD), can be safely studied using the stepping in place (SIP) task. However, clinical, visual identification of FOG during SIP is subjective and time consuming, and automatic FOG detection during SIP currently requires measuring the center of pressure on dual force plates. This study examines whether FOG elicited during SIP in 10 individuals with PD could be reliably detected using kinematic data measured from wearable inertial measurement unit sensors (IMUs). A general, logistic regression model (area under the curve = 0.81) determined that three gait parameters together were overall the most robust predictors of FOG during SIP: arrhythmicity, swing time coefficient of variation, and swing angular range. Participant-specific models revealed varying sets of gait parameters that best predicted FOG for each participant, highlighting variable FOG behaviors, and demonstrated equal or better performance for 6 out of the 10 participants, suggesting the opportunity for model personalization. The results of this study demonstrated that gait parameters measured from wearable IMUs reliably detected FOG during SIP, and the general and participant-specific gait parameters allude to variable FOG behaviors that could inform more personalized approaches for treatment of FOG and gait impairment in PD.

How resistant are levodopa‐resistant axial symptoms? Response of freezing, posture, and voice to increasing levodopa intestinal infusion rates in Parkinson disease

“…In parallel with the improvement of FoG, the gait analysis in our study demonstrated an improvement of stride length, turn duration, and turn velocity, confirming the strict relationship between FoG and specific gait parameters [ 37 , 38 , 39 ] and supporting the theory of a higher threshold for the improvement of axial motor symptoms when compared with appendicular symptoms like limb tremor, rigidity, and bradykinesia.…”

Proceedings of the Ninth Annual Deep Brain Stimulation Think Tank: Advances in Cutting Edge Technologies, Artificial Intelligence, Neuromodulation, Neuroethics, Pain, Interventional Psychiatry, Epilepsy, and Traumatic Brain Injury

“…Other relevant neural inputs include prolonged beta burst durations, which are related to disease severity, bradykinesia, and freezing of gait (FOG) (Tinkhauser et al, 2017;Anidi et al, 2018;Kehnemouyi et al, 2021). aDBS can also be driven by relevant behavioral inputs, such as tremor intensity or gait kinematics (Malekmohammadi et al, 2016;Cagnan et al, 2017;Herron et al, 2017;O'Day et al, 2020;Diep et al, 2021). The goal of therapy will determine the rates, at which DBS intensity is adjusted: slow ramps will adjust DBS intensity based on the time course of the onset and offset of medication doses, whereas faster ramps may respond to beta burst durations, and stochastic events such as tremor or FOG (Arlotti et al, 2018;Petrucci et al, 2020aPetrucci et al, ,b, 2021.…”

Technological support for people with Parkinson’s disease: a narrative review

“…DeepFOG has valid characteristics as an important telemedicine tool to inform doctors about the frequency of FoG events of their patients. In a further publication by Diep et al 41 used a FoG predicting technique in ten PD patients through the acquisition of kinematic data, measured by wearable shank-positioned Inertial measurement unit (IMU) sensors. Patient movements were analyzed during a stepping in place (SIP) test.…”