Development and validation of an accelerometer-based method for quantifying gait events.

An original signal processing algorithm is presented to automatically extract, on a stride-by-stride basis, four consecutive fundamental events of walking, heel strike (HS), toe strike (TS), heel-off (HO), and toe-off (TO), from wireless accelerometers applied to the right and left foot. First, the signals recorded from heel and toe three-axis accelerometers are segmented providing heel and toe flat phases. Then, the four gait events are defined from these flat phases. The accelerometer-based event identification was validated in seven healthy volunteers and a total of 247 trials against reference data provided by a force plate, a kinematic 3D analysis system, and video camera. HS, TS, HO, and TO were detected with a temporal accuracy ± precision of 1.3 ms ± 7.2 ms, -4.2 ms ± 10.9 ms, -3.7 ms ± 14.5 ms, and -1.8 ms ± 11.8 ms, respectively, with the associated 95% confidence intervals ranging from -6.3 ms to 2.2 ms. It is concluded that the developed accelerometer-based method can accurately and precisely detect HS, TS, HO, and TO, and could thus be used for the ambulatory monitoring of gait features computed from these events when measured concurrently in both feet.

Finger tapping clinimetric score prediction in Parkinson's disease using low-cost accelerometers.

The motor clinical hallmarks of Parkinson's disease (PD) are usually quantified by physicians using validated clinimetric scales such as the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). However, clinical ratings are prone to subjectivity and inter-rater variability. The PD medical community is therefore looking for a simple, inexpensive, and objective rating method. As a first step towards this goal, a triaxial accelerometer-based system was used in a sample of 36 PD patients and 10 age-matched controls as they performed the MDS-UPDRS finger tapping (FT) task. First, raw signals were epoched to isolate the successive single FT movements. Next, eighteen FT task movement features were extracted, depicting MDS-UPDRS features and accelerometer specific features. An ordinal logistic regression model and a greedy backward algorithm were used to identify the most relevant features in the prediction of MDS-UPDRS FT scores, given by 3 specialists in movement disorders (SMDs). The Goodman-Kruskal Gamma index obtained (0.961), depicting the predictive performance of the model, is similar to those obtained between the individual scores given by the SMD (0.870 to 0.970). The automatic prediction of MDS-UPDRS scores using the proposed system may be valuable in clinical trials designed to evaluate and modify motor disability in PD patients.

Brain activation pattern related to gait disturbances in Parkinson's disease.

Gait disturbances represent a therapeutic challenge in Parkinson's disease (PD). To further investigate their underlying pathophysiological mechanisms, we compared brain activation related to mental imagery of gait between 15 PD patients and 15 age-matched controls using a block-design functional MRI experiment. On average, patients showed altered locomotion relatively to controls, as assessed with a standardized gait test that evaluated the severity of PD-related gait disturbances on a 25-m path. The experiment was conducted in the subjects as they rehearsed themselves walking on the same path with a gait pattern similar as that during locomotor evaluation. Imagined walking times were measured on a trial-by-trial basis as a control of behavioral performance. In both groups, mean imagined walking time was not significantly different from that measured during real gait on the path used for evaluation. The between-group comparison of the mental gait activation pattern with reference to mental imagery of standing showed hypoactivations within parieto-occipital regions, along with the left hippocampus, midline/lateral cerebellum, and presumed pedunculopontine nucleus/mesencephalic locomotor area, in patients. More specifically, the activation level of the right posterior parietal cortex located within the impaired gait-related cognitive network decreased proportionally with the severity of gait disturbances scored on the path used for gait evaluation and mental imagery. These novel findings suggest that the right posterior parietal cortex dysfunction is strongly related to the severity of gait disturbances in PD. This region may represent a target for the development of therapeutic interventions for PD-related gait disturbances.

Gait feature extraction in Parkinson's disease using low-cost accelerometers.

The clinical hallmarks of Parkinson's disease (PD) are movement poverty and slowness (i.e. bradykinesia), muscle rigidity, limb tremor or gait disturbances. Parkinson's gait includes slowness, shuffling, short steps, freezing of gait (FoG) and/or asymmetries in gait. There are currently no validated clinical instruments or device that allow a full characterization of gait disturbances in PD. As a step towards this goal, a four accelerometer-based system is proposed to increase the number of parameters that can be extracted to characterize parkinsonian gait disturbances such as FoG or gait asymmetries. After developing the hardware, an algorithm has been developed, that automatically epoched the signals on a stride-by-stride basis and quantified, among others, the gait velocity, the stride time, the stance and swing phases, the single and double support phases or the maximum acceleration at toe-off, as validated by visual inspection of video recordings during the task. The results obtained in a PD patient and a healthy volunteer are presented. The FoG detection will be improved using time-frequency analysis and the system is about to be validated with a state-of-the-art 3D movement analysis system.