An objective and quantitative gait analysis system could, therefore, potentially improve the current practice (semiquantitative gait evaluation) that may aid in diagnosis, symptom monitoring, therapy management, rehabilitation and fall risk assessment and prevention in Parkinson’s disease patients. Some gait features in PD are specific, and get worse during the disease course. Gait impairment is an evolving condition and different patterns of gait disturbances can be detected throughout the progression of the disease : reduced amplitude of arm swing, reduced smoothness of locomotion, increased interlimb asymmetry, low speed, reduced step length, shuffling steps, increased double-limb support, increased cadence, defragmentation of turns (i.e., turning en block), problems with gait initiation, freezing of gait and reduced balance and postural control. In an effort to improve PD management and move towards a quantitative and home-oriented assessment and recognition of PD motor symptoms, different technologies have been used to evaluate bradykinesia, rigidity, tremor and axial symptoms. In line with cardinal motor symptoms, to date, gait problems are evaluated with semiquantitative rating scales like the unified Parkinson’s disease rating scale (UPDRS) or the movement disorders society unified Parkinson’s disease rating scale (MDS-UPDRS). During moderate and advanced stages, gait problems, like freezing of gait and reduced balance and postural control, become more evident and unlike cardinal motor symptoms, PD patients respond less to conventional therapy (i.e., oral L-DOPA). This stimulation may be pharmacological with levodopa or dopamine agonists, or provided by DBS (deep brain stimulation). During this stage, the brain becomes very sensitive to dopamine level fluctuations, and a continuous stimulation (instead of pulsatile drugs administration) may help in controlling motor fluctuations, dyskinesias and cardinal motor symptoms. However, during moderate and advanced stages, in addition to cardinal motor symptoms, the patient may show motor fluctuations and dyskinesia. In the early stages of PD, the most effective treatment to alleviate motor symptoms is oral L-DOPA. In addition, PD is a dynamic disease (i.e., symptoms changes during the disease course) that requires continuous adjustment of therapy. Based on the current diagnostic criteria, the diagnostic error rate is around 20%. The lack of objective and quantitative biomarkers for diagnosis and symptoms monitoring leads to significant direct and indirect healthcare cost. Parkinson’s’ disease (PD) gold standard for diagnosis and symptoms monitoring is based on clinical evaluation, which includes several subjective components. In addition, none of the reported algorithms and technologies has been validated in large scale, independent studies. Despite a large number of studies on the topic of objective gait analysis in PD, only a limited number of studies reported algorithms that were accurate enough deemed to be useful for diagnosis and symptoms monitoring. For motor status discrimination the gait analysis algorithms showed a balanced accuracy range of 90.8–100%, sensitivity of 92.5–100% and specificity of 88–100%. Gait analysis algorithms used for diagnosis showed a balanced accuracy range of 83.5–100%, sensitivity of 83.3–100% and specificity of 82–100%. Only those studies that reported at least 80% sensitivity and specificity were included. We selected studies that have either used technologies to distinguish PD patients from healthy subjects or stratified PD patients according to motor status or disease stages. We searched PubMed for studies published between 1 January 2005, and 30 August 2019 on gait analysis in PD. The aim of this review is to summarize that most relevant technologies used to evaluate gait features and the associated algorithms that have shown promise to aid diagnosis and symptom monitoring in Parkinson’s disease (PD) patients.
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