scholarly journals Recovery of dynamic stability during slips unaffected by arm swing in people with Parkinson’s Disease

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249303
Author(s):  
Tarique Siragy ◽  
Allen Hill ◽  
Julie Nantel
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Calculated Data ◽  
Step Length ◽  
Step Width ◽  
Arm Swing ◽  
Margin Of Stability ◽  
Recovery Response ◽  
Arm Elevation ◽  
Arm Motion

The arm elevation strategy assists in recovering stability during slips in healthy young and elderly individuals. However, in people with Parkinson’s Disease, one of the main motor symptoms affecting the upper limbs is reduced arm swing which intensifies throughout the course of the disease before becoming absent. This holds direct implications for these individuals when encountering slips as the arm elevation strategy is an integral component in the interlimb slip response to restore stability. Arm swing’s effect in recovering from slips in people with Parkinson’s Disease though remains unexamined. Twenty people with Parkinson’s Disease (63.78 ± 8.97 years) walked with restricted and unrestricted arm swing conditions on a dual-belt treadmill where slips were induced on the least and most affected sides. Data were collected on the CAREN Extended System (Motek Medical, Amsterdam, NL). The Margin of Stability, linear and angular trunk velocities, as well as step length, time, and width were calculated. Data were examined during the slipped step and recovery step. The restricted arm swing condition, compared to unrestricted, caused a faster step time during the slipped step. Compared to the most affected leg, the least affected had a wider step width during the slipped step. During the recovery step, the least affected leg had a larger anteroposterior Margin of Stability and longer step time than the most affected. No differences between our arm swing conditions suggests that the normal arm swing in our participants was not more effective at restoring stability after an induced slip compared to when their arm motion was restricted. This may be due to the arm elevation strategy being ineffective in counteracting the slip’s backward destabilization in these individuals. Differences between the legs revealed that our participants were asymmetrically impaired in their slip recovery response.

scholarly journals Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson’s Disease: A Comprehensive Machine Learning Approach

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Rana Zia Ur Rehman ◽  
Silvia Del Din ◽  
Yu Guan ◽  
Alison J. Yarnall ◽  
Jian Qing Shi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Decision Making ◽  
Information Gain ◽  
Step Length ◽  
Support Vector ◽  
Step Width ◽  
Gait Characteristics

AbstractParkinson’s disease (PD) is the second most common neurodegenerative disease; gait impairments are typical and are associated with increased fall risk and poor quality of life. Gait is potentially a useful biomarker to help discriminate PD at an early stage, however the optimal characteristics and combination are unclear. In this study, we used machine learning (ML) techniques to determine the optimal combination of gait characteristics to discriminate people with PD and healthy controls (HC). 303 participants (119 PD, 184 HC) walked continuously around a circuit for 2-minutes at a self-paced walk. Gait was quantified using an instrumented mat (GAITRite) from which 16 gait characteristics were derived and assessed. Gait characteristics were selected using different ML approaches to determine the optimal method (random forest with information gain and recursive features elimination (RFE) technique with support vector machine (SVM) and logistic regression). Five clinical gait characteristics were identified with RFE-SVM (mean step velocity, mean step length, step length variability, mean step width, and step width variability) that accurately classified PD. Model accuracy for classification of early PD ranged between 73–97% with 63–100% sensitivity and 79–94% specificity. In conclusion, we identified a subset of gait characteristics for accurate early classification of PD. These findings pave the way for a better understanding of the utility of ML techniques to support informed clinical decision-making.

scholarly journals Turning Characteristics of the More-Affected Side in Parkinson’s Disease Patients with Freezing of Gait

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3098
Author(s):  
Hwayoung Park ◽  
Changhong Youm ◽  
Myeounggon Lee ◽  
Byungjoo Noh ◽  
Sang-Myung Cheon
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Center Of Mass ◽  
Freezing Of Gait ◽  
Step Length ◽  
Control Group ◽  
Maximum Speed ◽  
Step Width ◽  
Affected Side ◽  
Anterior Posterior

This study investigated the turning characteristics of the more-affected limbs in Parkinson’s disease (PD) patients in comparison with that of a control group, and in PD patients with freezing of gait (FOG; freezers) in comparison with those without FOG (non-freezers) for 360° and 540° turning tasks at the maximum speed. A total of 12 freezers, 12 non-freezers, and 12 controls participated in this study. The PD patients showed significantly longer total durations, shorter inner and outer step lengths, and greater anterior–posterior (AP) root mean square (RMS) center of mass (COM) distances compared to those for the controls. The freezers showed significantly greater AP and medial-lateral (ML) RMS COM distances compared to those of non-freezers. The turning task toward the inner step of the more-affected side (IMA) in PD patients showed significantly greater step width, total steps, and AP and ML RMS COM distances than that toward the outer step of the more-affected side (OMA). The corresponding results for freezers revealed significantly higher total steps and shorter inner step length during the 540° turn toward the IMA than that toward the OMA. Therefore, PD patients and freezers exhibited greater turning difficulty in performing challenging turning tasks such as turning with an increased angle and speed and toward the more-affected side.

scholarly journals Gait and Balance Changes with Investigational Peripheral Nerve Cell Therapy during Deep Brain Stimulation in People with Parkinson’s Disease

2021 ◽  
Vol 11 (4) ◽  
pp. 500
Author(s):  
Geetanjali Gera ◽  
Zain Guduru ◽  
Tritia Yamasaki ◽  
Julie A. Gurwell ◽  
Monica J. Chau ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Peripheral Nerve ◽  
Brain Stimulation ◽  
Step Length ◽  
Gait Velocity ◽  
Balance Performance ◽  
Improved Performance ◽  
Deep Brain

Background: The efficacy of deep brain stimulation (DBS) and dopaminergic therapy is known to decrease over time. Hence, a new investigational approach combines implanting autologous injury-activated peripheral nerve grafts (APNG) at the time of bilateral DBS surgery to the globus pallidus interna. Objectives: In a study where APNG was unilaterally implanted into the substantia nigra, we explored the effects on clinical gait and balance assessments over two years in 14 individuals with Parkinson’s disease. Methods: Computerized gait and balance evaluations were performed without medication, and stimulation was in the off state for at least 12 h to best assess the role of APNG implantation alone. We hypothesized that APNG might improve gait and balance deficits associated with PD. Results: While people with a degenerative movement disorder typically worsen with time, none of the gait parameters significantly changed across visits in this 24 month study. The postural stability item in the UPDRS did not worsen from baseline to the 24-month follow-up. However, we measured gait and balance improvements in the two most affected individuals, who had moderate PD. In these two individuals, we observed an increase in gait velocity and step length that persisted over 6 and 24 months. Conclusions: Participants did not show worsening of gait and balance performance in the off therapy state two years after surgery, while the two most severely affected participants showed improved performance. Further studies may better address the long-term maintanenace of these results.

Gait velocity and step length at baseline predict outcome of Nordic walking training in patients with Parkinson's disease

2015 ◽  
Vol 21 (4) ◽  
pp. 413-416 ◽  
Author(s):  
Manon Herfurth ◽  
Jana Godau ◽  
Barbara Kattner ◽  
Silvia Rombach ◽  
Stefan Grau ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Step Length ◽  
Gait Velocity ◽  
Nordic Walking

Step length predicts executive dysfunction in Parkinson’s disease: a 3-year prospective study

2018 ◽  
Vol 265 (10) ◽  
pp. 2211-2220 ◽  
Author(s):  
Marianna Amboni ◽  
Luigi Iuppariello ◽  
Alessandro Iavarone ◽  
Alfonso Fasano ◽  
Raffaele Palladino ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Prospective Study ◽  
Executive Dysfunction ◽  
Step Length

scholarly journals Gait Training Methods and ChangesiIn Gait Parameters in Parkinson`s Disease (Literature Review)

2020 ◽  
Vol 2 (7) ◽  
Author(s):  
Aušra Stuopelytė ◽  
Rasa Šakalienė
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Step Length ◽  
Gait Training ◽  
Auditory Stimulation ◽  
Training Methods ◽  
Dual Tasking ◽  
Gait Parameters ◽  
Rhythmic Auditory Stimulation ◽  
Walking Velocity

Parkinson’s disease is a chronic progressive neurological disorder that can impact function to a variable degree. Changes in gait parameters are the most common signs of Parkinson’s disease. Patients with Parkinson’s disease walk with a reduced step length, step time, walking velocity and walking cadence, increased stride cycle time, coefficient of variation of the step amplitude and step time and increased risk to fall. So, various gait training methods are applied. The effect of rhythmic auditory stimulation on gait in Parkinson’s disease patients is analysed. We can use various kinds of music, metronome, scansion and clapping as a rhythmic auditory stimulation.One of gait training methods in Parkinson’s disease patients is treadmill training. There are attempts to combine treadmill training with transcranial magnetic stimulation and virtual reality. We can use Nordic walking method and because walking technique requires straight posture, trunk rotation, bigger step and heel stride. More often robot–assisted gait training is used in patients with Parkinson’s disease gait training. The effect of dual–tasking and walking with music methods for gait and balance training in patients with Parkinson’s disease is also analysed. This method requires participants to perform primary and secondary tasks at the same time. The secondary task can be cognitive or motor. Dual–tasking is widely analysed because opinions about applying this method are very controversial. Walking with music method is more often analysed in scientific literature. This method could not be compared to rhythmic auditory stimulation method because the latter requires precise walking to rhythm and walking with music method is oriented to emotional component (music is chosen according patients’ music taste). As these methods are applied, we can see an increase in chosen walking and maximal walking velocities, step length and time, distance covered, and decrease in the coefficient of variation of the step time and turning time.Keywords: Gait impairments, walking velocity, rhythmic auditory stimulation.

scholarly journals Clinical Differentiation of Essential Tremor and Parkinson's Disease

2013 ◽  
Vol 6 ◽  
pp. CCRep.S11903 ◽  
Author(s):  
Robert Fekete ◽  
Jin Li
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Essential Tremor ◽  
Single Photon ◽  
Photon Emission ◽  
Emission Computed Tomography ◽  
Postural Tremor ◽  
Arm Swing ◽  
Rest Tremor ◽  
Dopaminergic Terminal

We present clinical features and tremor characterization in a patient with Parkinson's disease (PD) as well as in two cases of essential tremor (ET) with some parkinsonian features but no evidence of dopaminergic terminal loss on 123I-FP-CIT Single Photon Emission Computed Tomography (SPECT). Relatively slow frequency rest tremor and bilateral upper extremity bradykinesia without decrementing amplitude were observed in the ET cases, with unilaterally decreased arm swing in case 3. Alternating rest tremor and re-emergent tremor with 13 second latency was confirmed in the PD case. Re-emergent tremor had alternating characteristics, which to our knowledge has not been previously reported. The ET cases had synchronous postural tremor. Alternating re-emergent tremor in PD provides further evidence for re-emergent tremor as an analogue of rest tremor in PD. Two cases of ET with synchronous postural tremor and one to two year history of parkinsonian features had no evidence of dopaminergic terminal loss up to 40 years after the initial onset of ET. Tremor synchronicity characterization can assist in differential diagnosis between the two disorders.

scholarly journals Quantification of Arm Swing during Walking in Healthy Adults and Parkinson’s Disease Patients: Wearable Sensor-Based Algorithm Development and Validation

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5963 ◽  
Author(s):  
Elke Warmerdam ◽  
Robbin Romijnders ◽  
Julius Welzel ◽  
Clint Hansen ◽  
Gerhard Schmidt ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Angular Velocity ◽  
Wearable Sensors ◽  
Healthy Adults ◽  
Wearable Sensor ◽  
Arm Swing ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Development And Validation

Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson’s disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from −0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson’s disease.

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