Measuring freezing of gait during daily-life: an open-source, wearable sensors approach

Abstract

Background Although a growing number of studies focus on the measurement and detection of freezing of gait (FoG) in laboratory settings, only a few studies have attempted to measure FoG during daily life with body-worn sensors. Here, we presented a novel algorithm to detect FoG in a group of people with Parkinson’s disease (PD) in the laboratory (Study I) and extended the algorithm in a second cohort of people with PD at home during daily life (Study II). Methods In Study I, we described of our novel FoG detection algorithm based on five inertial sensors attached to the feet, shins and lumbar region while walking in 40 participants with PD. We compared the performance of the algorithm with two expert clinical raters who scored the number of FoG episodes from video recordings of walking and turning based on duration of the episodes: very short (< 1 s), short (2–5 s), and long (> 5 s). In Study II, a different cohort of 48 people with PD (with and without FoG) wore 3 wearable sensors on their feet and lumbar region for 7 days. Our primary outcome measures for freezing were the % time spent freezing and its variability. Results We showed moderate to good agreement in the number of FoG episodes detected in the laboratory (Study I) between clinical raters and the algorithm (if wearable sensors were placed on the feet) for short and long FoG episodes, but not for very short FoG episodes. When extending this methodology to unsupervised home monitoring (Study II), we found that percent time spent freezing and the variability of time spent freezing differentiated between people with and without FoG (p < 0.05), and that short FoG episodes account for 69% of the total FoG episodes. Conclusion Our findings showed that objective measures of freezing in PD using inertial sensors on the feet in the laboratory are matching well with clinical scores. Although results found during daily life are promising, they need to be validated. Objective measures of FoG with wearable technology during community-living would be useful for managing this distressing feature of mobility disability in PD.

Alterations in intermuscular coordination underlying isokinetic exercise after a stroke and their implications on neurorehabilitation

“…Since it was also reported that the altered activation of lower limb muscle synergies could contribute to the reduced complexity of intermuscular coordination underlying free gait after stroke [ 17 ], this method might be applicable to restoring the stroke-affected intermuscular coordination for gait motions despite the difference in exercise condition. Considering the correlation between the complexity of intermuscular coordination and the gait quality measures in the stroke-affected lower limbs [ 31 ], gait performance might be improved by recovering the complexity of intermuscular coordination through the proposed training. In addition, a separate training regime to reduce the abnormal co-activation of the deltoid muscles can be accompanied by the training of new synergy activation profiles to rehabilitate stroke-affected intermuscular coordination of upper limb muscles effectively.…”

Whole-Body Movements Increase Arm Use Outcomes of Wrist-Worn Accelerometers in Stroke Patients

“…These potential problems have been identified by previous studies, and different methods have already been proposed to correct arm use measurements with wrist-worn accelerometers for the effect of whole-body movements. These methods are measuring arm movements during sitting and standing [ 13 , 15 ], or calculating a ratio outcome between arms [ 14 ]. However, these methods are not widely adopted in the research field, because (1) they require more complex sensor set-ups and/or signal analyses, and (2) the effect of whole-body movements on arm use measurements with wrist-worn accelerometers is still unclear and has not yet been quantified.…”

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

“…Instead, wearable inertial measurement unit sensors (IMUs) are small and portable, allowing for use in daily life. IMUs have been used to characterize and predict FOG during several tasks [9,, and many of these IMU-based FOG detection schemes rely on statistical and machine learning methods [9,[11][12][13][14][15][16][17][18][19][20][21][22]. For example, IMUs measured kinematic data during straight walking and the TBC [9,37].…”