Inertial Sensor-Based Gait Analysis for Evaluating the Effects of Acupuncture Treatment in Parkinson’s Disease

Abstract Background Gait impairments are among the most common and impactful symptoms of Parkinson’s disease (PD). Recent technological advances aim to quantify these impairments using low-cost wearable systems for use in either supervised clinical consultations or long-term unsupervised monitoring of gait in ecological environments. However, very few of these wearable systems have been validated comparatively to a criterion of established validity. Objective We developed two movement analysis solutions (3D full-body kinematics based on inertial sensors, and a smartphone application) in which validity was assessed versus the optoelectronic criterion in a population of PD patients. Methods Nineteen subjects with PD (7 female) participated in the study (age: 62 ± 12.27 years; disease duration: 6.39 ± 3.70 years; HY: 2 ± 0.23). Each participant underwent a gait analysis whilst barefoot, at a self-selected speed, for a distance of 3 times 10 m in a straight line, assessed simultaneously with all three systems. Results Our results show excellent agreement between either solution and the optoelectronic criterion. Both systems differentiate between PD patients and healthy controls, and between PD patients in ON or OFF medication states (normal difference distributions pooled from published research in PD patients in ON and OFF states that included an age-matched healthy control group). Fair to high waveform similarity and mean absolute errors below the mean relative orientation accuracy of the equipment were found when comparing the angular kinematics between the full-body inertial sensor-based system and the optoelectronic criterion. Conclusions We conclude that the presented solutions produce accurate results and can capture clinically relevant parameters using commodity wearable sensors or a simple smartphone. This validation will hopefully enable the adoption of these systems for supervised and unsupervised gait analysis in clinical practice and clinical trials.

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Hidden Markov Model based Stride Segmentation on Unsupervised Free-living Gait Data in Parkinson’s Disease Patients

10.21203/rs.3.rs-269965/v1 ◽

2021 ◽

Author(s):

Nils Roth ◽

Arne Küderle ◽

Martin Ullrich ◽

Till Gladow ◽

Franz Marxreiter ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Markov Model ◽

Hidden Markov Model ◽

Gait Analysis ◽

Large Scale ◽

Hidden Markov ◽

Inertial Sensor ◽

Training Data ◽

Free Living

Abstract Background: To objectively assess a patient's gait, a robust identification of stride borders is one of the first steps in inertial sensor-based mobile gait analysis pipelines. While many different methods for stride segmentation have been presented in the literature, an out-of-lab evaluation of respective algorithms on free-living gait is still missing. Method : To address this issue, we present a comprehensive free-living evaluation dataset, including 146.574 semi-automatic labeled strides of 28 Parkinson's Disease patients. This dataset was used to evaluate the segmentation performance of a new Hidden Markov Model (HMM) based stride segmentation approach compared to an available dynamic time warping (DTW) based method. Results: The proposed HMM achieved a mean F1-score of 92.1% and outperformed the DTW approach significantly. Further analysis revealed a dependency of segmentation performance to the number of strides within respective walking bouts. Shorter bouts (<30 strides) resulted in worse performance, which could be related to more heterogeneous gait and an increased diversity of different stride types in short free-living walking bouts. In contrast, the HMM reached F1-scores of more than 96.2% for longer bouts (>50 strides). Furthermore, we showed that an HMM, which was trained on at-lab data only, could be transferred to a free-living context with a negligible decrease in performance. Conclusion: The generalizability of the proposed HMM is a promising feature, as fully labeled free-living training data might not be available for many applications. To the best of our knowledge, this is the first evaluation of stride segmentation performance on a large scale free-living dataset. Our proposed HMM-based approach was able to address the increased complexity of free-living gait data, and thus will help to enable a robust assessment of stride parameters in future free-living gait analysis applications

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Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson’s disease patients

Journal of NeuroEngineering and Rehabilitation ◽

10.1186/s12984-021-00883-7 ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Nils Roth ◽

Arne Küderle ◽

Martin Ullrich ◽

Till Gladow ◽

Franz Marxreiter ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Markov Model ◽

Hidden Markov Model ◽

Gait Analysis ◽

Large Scale ◽

Hidden Markov ◽

Inertial Sensor ◽

Training Data ◽

Free Living

Abstract Background To objectively assess a patient’s gait, a robust identification of stride borders is one of the first steps in inertial sensor-based mobile gait analysis pipelines. While many different methods for stride segmentation have been presented in the literature, an out-of-lab evaluation of respective algorithms on free-living gait is still missing. Method To address this issue, we present a comprehensive free-living evaluation dataset, including 146.574 semi-automatic labeled strides of 28 Parkinson’s Disease patients. This dataset was used to evaluate the segmentation performance of a new Hidden Markov Model (HMM) based stride segmentation approach compared to an available dynamic time warping (DTW) based method. Results The proposed HMM achieved a mean F1-score of 92.1% and outperformed the DTW approach significantly. Further analysis revealed a dependency of segmentation performance to the number of strides within respective walking bouts. Shorter bouts ($$< 30$$ < 30 strides) resulted in worse performance, which could be related to more heterogeneous gait and an increased diversity of different stride types in short free-living walking bouts. In contrast, the HMM reached F1-scores of more than 96.2% for longer bouts ($$> 50$$ > 50 strides). Furthermore, we showed that an HMM, which was trained on at-lab data only, could be transferred to a free-living context with a negligible decrease in performance. Conclusion The generalizability of the proposed HMM is a promising feature, as fully labeled free-living training data might not be available for many applications. To the best of our knowledge, this is the first evaluation of stride segmentation performance on a large scale free-living dataset. Our proposed HMM-based approach was able to address the increased complexity of free-living gait data, and thus will help to enable a robust assessment of stride parameters in future free-living gait analysis applications.

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Effects of Onabotulinum Toxin A on Gait in Parkinson’s Disease Patients with Foot Dystonia

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques ◽

10.1017/cjn.2021.42 ◽

2021 ◽

pp. 1-6

Author(s):

Pei Huang ◽

Yuan-Yuan Li ◽

Jung E. Park ◽

Ping Huang ◽

Qin Xiao ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Botulinum Toxin ◽

Gait Analysis ◽

Rating Scale ◽

Analog Scale ◽

Toxin A ◽

Timed Up And Go ◽

Balance Test ◽

Onabotulinum Toxin A

ABSTRACT: We investigated the effects of botulinum toxin on gait in Parkinson’s disease (PD) patients with foot dystonia. Six patients underwent onabotulinum toxin A injection and were assessed by Burke–Fahn–Marsden Dystonia Rating Scale (BFMDRS), visual analog scale (VAS) of pain, Timed Up and Go (TUG), Berg Balance Test (BBT), and 3D gait analysis at baseline, 1 month, and 3 months. BFMDRS (p = 0.002), VAS (p = 0.024), TUG (p = 0.028), and BBT (p = 0.034) were improved. Foot pressures at Toe 1 (p = 0.028) and Midfoot (p = 0.018) were reduced, indicating botulinum toxin’s effects in alleviating the dystonia severity and pain and improving foot pressures during walking in PD.

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Gait analysis for early Parkinson’s disease detection based on deep learning

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2019-0003 ◽

2019 ◽

Vol 5 (1) ◽

pp. 9-12

Author(s):

Jyothsna Kondragunta ◽

Christian Wiede ◽

Gangolf Hirtz

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Deep Learning ◽

Gait Analysis ◽

Pose Estimation ◽

Early Identification ◽

Human Gait ◽

3D Pose Estimation ◽

Wearable Technologies ◽

Advantages And Disadvantages

AbstractBetter handling of neurological or neurodegenerative disorders such as Parkinson’s Disease (PD) is only possible with an early identification of relevant symptoms. Although the entire disease can’t be treated but the effects of the disease can be delayed with proper care and treatment. Due to this fact, early identification of symptoms for the PD plays a key role. Recent studies state that gait abnormalities are clearly evident while performing dual cognitive tasks by people suffering with PD. Researches also proved that the early identification of the abnormal gaits leads to the identification of PD in advance. Novel technologies provide many options for the identification and analysis of human gait. These technologies can be broadly classified as wearable and non-wearable technologies. As PD is more prominent in elderly people, wearable sensors may hinder the natural persons movement and is considered out of scope of this paper. Non-wearable technologies especially Image Processing (IP) approaches captures data of the person’s gait through optic sensors Existing IP approaches which perform gait analysis is restricted with the parameters such as angle of view, background and occlusions due to objects or due to own body movements. Till date there exists no researcher in terms of analyzing gait through 3D pose estimation. As deep leaning has proven efficient in 2D pose estimation, we propose an 3D pose estimation along with proper dataset. This paper outlines the advantages and disadvantages of the state-of-the-art methods in application of gait analysis for early PD identification. Furthermore, the importance of extracting the gait parameters from 3D pose estimation using deep learning is outlined.

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Gait analysis in patients with Parkinson's disease and motor fluctuations: Influence of levodopa and comparison with other measures of motor function

Movement Disorders ◽

10.1002/mds.870130607 ◽

1998 ◽

Vol 13 (6) ◽

pp. 900-906 ◽

Cited By ~ 66

Author(s):

John D. O'Sullivan ◽

Catherine M. Said ◽

Louise C. Dillon ◽

Marion Hoffman ◽

Andrew J. Hughes

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Gait Analysis ◽

Motor Function ◽

Motor Fluctuations

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Gait analysis in three different 6-hydroxydopamine rat models of Parkinson's disease

Neuroscience Letters ◽

10.1016/j.neulet.2014.10.032 ◽

2015 ◽

Vol 584 ◽

pp. 184-189 ◽

Cited By ~ 30

Author(s):

Ming Zhou ◽

Wangming Zhang ◽

Jingyu Chang ◽

Jun Wang ◽

Weixin Zheng ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Gait Analysis ◽

Rat Models ◽

6 Hydroxydopamine

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Association between Objectively Measured Physical Activity and Gait Patterns in People with Parkinson’s Disease: Results from a 3-Month Monitoring

Parkinson s Disease ◽

10.1155/2018/7806574 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10

Author(s):

Micaela Porta ◽

Giuseppina Pilloni ◽

Roberta Pili ◽

Carlo Casula ◽

Mauro Murgia ◽

...

Keyword(s):

Physical Activity ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Gait Analysis ◽

Time Slot ◽

Moderate Intensity ◽

Kinematic Parameters ◽

Gait Patterns ◽

Flexion Extension ◽

Vigorous Intensity

Background. Although physical activity (PA) is known to be beneficial in improving motor symptoms of people with Parkinson’s disease (pwPD), little is known about the relationship between gait patterns and features of PA performed during daily life. Objective. To verify the existence of possible relationships between spatiotemporal and kinematic parameters of gait and amount/intensity of PA, both instrumentally assessed. Methods. Eighteen individuals affected by PD (10F and 8M, age 68.0 ± 10.8 years, 1.5 ≤ Hoehn and Yahr (H&Y) < 3) were required to wear a triaxial accelerometer 24 h/day for 3 consecutive months. They also underwent a 3D computerized gait analysis at the beginning and end of the PA assessment period. The number of daily steps and PA intensity were calculated on the whole day, and the period from 6:00 to 24:00 was grouped into 3 time slots, using 3 different cut-point sets previously validated in the case of both pwPD and healthy older adults. 3D gait analysis provided spatiotemporal and kinematic parameters of gait, including summary indexes of quality (Gait Profile Score (GPS) and Gait Variable Score (GVS)). Results. The analysis of hourly trends of PA revealed the existence of two peaks located in the morning (approximately at 10) and in the early evening (between 18 and 19). However, during the morning time slot (06:00–12:00), pwPD performed significantly higher amounts of steps (4313 vs. 3437 in the 12:00–18:00 time slot, p<0.001, and vs. 2889 in the 18:00–24:00 time slot, p=0.021) and of moderate-to-vigorous PA (43.2% vs. 36.3% in the 12:00–18:00 time slot, p=0.002, and vs. 31.4% in the 18:00–24:00 time slot, p=0.049). The correlation analysis shows that several PA intensity parameters are significantly associated with swing-phase duration (rho = −0.675 for sedentary intensity, rho = 0.717 for moderate-to-vigorous intensity, p<0.001), cadence (rho = 0.509 for sedentary intensity, rho = −0.575 for moderate-to-vigorous intensity, p<0.05), and overall gait pattern quality as expressed by GPS (rho = −0.498 to −0.606 for moderate intensity, p<0.05) and GVS of knee flexion-extension (rho = −0.536 for moderate intensity, p<0.05). Conclusions. Long-term monitoring of PA integrated by the quantitative assessment of spatiotemporal and kinematic parameters of gait may represent a useful tool in supporting a better-targeted prescription of PA and rehabilitative treatments in pwPD.

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