Gait Analysis and Severity Estimation of Parkinson’s Disease Patients Using Insole-Type Pressure Sensors

Objective: The object of the study was to evaluate the efficacy of Proprioceptive Focal Stimulation on Gait in middle—advanced Parkinson (PD) patients by a crossover, randomized, double Blind double dummy study using Equistasi®, a nano-technological device of the dimension of a plaster which generates High Frequency Vibration (FV).Background: The efficacy of Gait Analysis (GA) on evaluating gait modification on Parkinson's disease (PD) Patients is already well-known. Therefore, GA was recorded in a group of PD patients using Equistasi® device and its placebo.Methods: Forty PD patients on optimal therapy were enrolled in the study. Patients were randomly assigned to receive active or sham stimulation for 8 weeks and, following a wash-out period, switched to an additional 8-week period with the reverse intervention. GA was performed at baseline and at the end of both 8-weeks treatment periods Clinical state was monitored by MDUPDRS part III.Results: Active stimulation induced a significant improvement in Mean Velocity (Velocity), Stride Length (SL), Stance (STA), and Double Support (DST) percentage, both in left and right stride. The ANOVA analysis using H&Y stage as a factor, showed that DST and MDUPDRS III scores improved significantly more in the more severely affected subjects.Conclusions: The findings obtained in this randomized controlled study show the efficacy of mechanical focal vibration, as stimulation of the proprioceptive system, in PD and encourage further investigation. The effect of the device on more severe patients may open a new possibility to identify the most appropriate candidate for the management of gait disturbances and postural instability with FV delivered with Equistasi®.

BackgroundEnd Stage Renal Disease (ESRD) patients undergoing hemodialysis frequently experience sensory and motor peripheral nerve function changes, which can impair mobility and elevate the risk of falls.1 In May 2019,...

Flexible sensing systems (FSSs) designed to measure plantar pressure can deliver instantaneous feedback on human movement and posture. This feedback is crucial not only for preventing and controlling diseases associated with abnormal plantar pressures but also for optimizing athletes' postures to minimize injuries. The development of an optimal plantar pressure sensor hinges on key metrics such as a wide sensing range, high sensitivity, and long-term stability. However, the effectiveness of current flexible sensors is impeded by numerous challenges, including limitations in structural deformability, mechanical incompatibility between multifunctional layers, and instability under complex stress conditions. Addressing these limitations, we have engineered an integrated pressure sensing system with high sensitivity and reliability for human plantar pressure and gait analysis. It features a high-modulus, porous laminated ionic fiber structure with robust self-bonded interfaces, utilizing a unified polyimide material system. This system showcases a high sensitivity (156.6 kPa-1), an extensive sensing range (up to 4000 kPa), and augmented interfacial toughness and durability (over 150,000 cycles). Additionally, our FSS is capable of real-time monitoring of plantar pressure distribution across various sports activities. Leveraging deep learning, the flexible sensing system achieves a high-precision, intelligent recognition of different plantar types with a 99.8% accuracy rate. This approach provides a strategic advancement in the field of flexible pressure sensors, ensuring prolonged stability and accuracy even amidst complex pressure dynamics and providing a feasible solution for long-term gait monitoring and analysis.

Foot plantar pressure provides plenty of information for gait research and medical diagnostics. Gait analysis can be used to evaluate stroke patient’s mobility and rehabilitation status. However, most of existing gait analysis system can only be used in laboratory or indoor occasions. It makes a large limitation for the gait data collection and analysis. This paper presents a novel wearable human gait analysis system based on flexible circuit and piezoresistive pressure sensors. The insole embedded with 8 pressure sensors is fabricated to collect dynamic resistance varying signals due to the piezoresistive effect. Then the resistance signal is converted to voltage signals with a resistance-voltage conversion circuit board. The wireless transmitter sends the gait data to computer for real-time gait analysis via WIFI chip. The experiment results show the pressure difference on different area of foot plantar during walking, running and squatting. And several gait characteristics such as peak-peak voltage and mean voltage are also calculated and compared. It shows that this novel wearable insole device can be used to monitor plantar pressure during daily life effectively.

Gait analysis is crucial for the detection and management of various neurological and musculoskeletal disorders. The identification of gait events is valuable for enhancing gait analysis, developing accurate monitoring systems, and evaluating treatments for pathological gait. The aim of this work is to introduce the Smart-Insole Dataset to be used for the development and evaluation of computational methods focusing on gait analysis. Towards this objective, temporal and spatial characteristics of gait have been estimated as the first insight of pathology. The Smart-Insole dataset includes data derived from pressure sensor insoles, while 29 participants (healthy adults, elderly, Parkinson’s disease patients) performed two different sets of tests: The Walk Straight and Turn test, and a modified version of the Timed Up and Go test. A neurologist specialized in movement disorders evaluated the performance of the participants by rating four items of the MDS-Unified Parkinson’s Disease Rating Scale. The annotation of the dataset was performed by a team of experienced computer scientists, manually and using a gait event detection algorithm. The results evidence the discrimination between the different groups, and the verification of established assumptions regarding gait characteristics of the elderly and patients suffering from Parkinson’s disease.

The aim of this study was to investigate the gait spatiotemporal, kinematic, and kinetic changes of Parkinson's disease (PD) patient with freezing of gait (FOG) under the laser cue (LC). Such an approach may provide greater insight into the effects of LC on gait. Thirty-four PD with FOG (PD + FOG) and 32 healthy controls (HC) were tested in gait laboratory. Patients were tested at their usual self-selected speed in no laser cue (NC) first and then under LC condition. Sagittal plane kinematic and kinetic parameters of the lower-limb joints (hip, knee, and ankle joints) as well as spatiotemporal parameters (velocity, cadence, stride length, single and double support time), were measured. Spatiotemporal parameters and kinematic were submitted to one-way analysis of variance (ANOVA) to explore difference among NC, LC, and HC. Covariance analysis was used to compare kinetic parameters. For PD + FOG, spatiotemporal parameters (stride length, velocity, and cadence) were significantly improved in LC (1.06±0.18, 1.01±0.19, 120±13.26, respectively) compared with NC (0.93±0.20, 0.87±0.17, 131±14.75) (P=0.027, 0.045, 0.035, respectively), and close to HC (1.1±0.12, 1.12±0.13, 116±9.37) (P=0.594, 0.276, 0.084, respectively). In kinematics, LC could significantly ameliorate the amplitude of maximal dorsiflexion in ankle (35.1±3.8), extension in stance in knee (16.8±4.3) and hip (4.43±5.1), as well as the range of motion (ROM) in ankle (33.15±6.1) and hip joints (38.6±3.3). In kinetics, LC also markedly improved power generation in ankle (2.03±1.52) and hip joints (1.08±0.48) and power absorption in pre-swing phase in knee joint (-1.68±0.29) compared with NC (1.37±1.13, 0.899±0.43, -1.31±0.27, respectively). LC significantly improves gait performance in spatiotemporal parameters as well as kinematics and kinetics performance in ankle and hip joints. LC may be promising when applied as an optional technique in the rehabilitation training in PD + FOG.

Does gait analysis quantify motor rehabilitation efficacy in Parkinson's disease patients?

Gait analysis provides valuable motor deficit quantitative information about Parkinson’s disease patients. Detection of gait abnormalities is key to preserving healthy mobility. The goal of this paper is to propose a novel gait analysis and continuous wavelet transform-based approach to diagnose idiopathic Parkinson’s disease. First, we eliminate the noise resulting from orientation changes of test subjects by filtering the continuous wavelet transform output below 0.8 Hz. Next, we analyze the complex plot output above 0.8 Hz, which takes an ellipse, and calculate the area using $$95\%$$ confidence level. We found out that this ellipse area, along with the mean continuous wavelet transform output value, and the peak of the temporal signal are excellent features for classification. Experiments using Artificial Neural Networks on the Physionet database produced an accuracy of $$97.6\%$$ . Furthermore, we have shown an association between the Parkinson’s disease severity stage and the ellipse complex plot area with a 97.8% overall accuracy. Based on the results, we could effectively recognize the gait patterns and distinguish apart Parkinson’s disease patients with varying severity from healthy individuals.

Simple SummaryTo study effects of housing conditions on the claw health of dairy cows, objective gait analysis methods can be useful. In this study, a novel mobile pressure sensor system, attached under the claws of the hind limbs of dairy cows, was used for the first time. Additionally, inertial measurement units (IMUs) in combination with a newly developed automatic step detection algorithm were used. Gait analysis was performed in ten dairy cows, walking and trotting on concrete and rubber mats. The results showed the applicability of the objective gait analysis methods in dairy cows. Analysis of pressure under the claws revealed a significantly higher load in cows moving on concrete compared to rubber mats. The development of objective methods should be used to gain knowledge about factors that impair claw health. Sensor-based research should be applied to improve animal welfare by evaluating the housing environment objectively and better adapt it to the cows’ needs.Mechanical overburdening is a major risk factor that provokes non-infectious claw diseases. Moreover, lameness-causing lesions often remain undetected and untreated. Therefore, prevention of claw tissue overburdening is of interest, especially by analyzing harmful effects within dairy cows’ housing environment. However, objective “on-cow” methods for bovine gait analysis are underdeveloped. The purpose of the study was to apply an innovative mobile pressure sensor system attached at the claws to perform pedobarometric gait analysis. A further goal was the supplementation with accelerative data, generated simultaneously by use of two inertial measurement units (IMUs), attached at metatarsal level. IMU data were analyzed with an automatic step detection algorithm. Gait analysis was performed in ten dairy cows, walking and trotting on concrete flooring and rubber mats. In addition to the basic applicability of the sensor systems and with the aid of the automatic step detection algorithm for gait analysis in cows, we were able to determine the impact of the gait and flooring type on kinematic and kinetic parameters. For pressure sensor output, concrete was associated with significantly (p < 0.001) higher maximum and average pressure values and a significantly smaller contact area, compared to rubber mats. In contrast to walking, trotting led to a significantly higher force, especially under the medial claw. Further, IMU-derived parameters were significantly influenced by the gait. The described sensor systems are useful tools for detailed gait analysis in dairy cows. They allow the investigation of factors which may affect claw health negatively.

Frozen gait (FOG) is one of the most disabling motor symptoms associated with Parkinson’s disease. As a result, these patients are unable to walk for an extended period of time and have a significant amount of imbalance. Additionally, the periodic phases of gait of these patients are incorrect. This could imply a decrease in daily activities. This is a plot study in which healthy volunteers' gait analysis is used to classify the four phases of the gait cycle: foot flat (FOF), heel rise (HER), toe off (TOF), and heel strike (HES). The gait dataset was created by analyzing the 16 force sensors and three inertial measurement unit (IMU) sensors on each leg. We then applied and compared these features to three different types of classifiers: support vector machine (SVM), k-nearest neighbors (kNN), and multilayer perceptron (MLP). When the number of nodes in a single hidden layer exceeds 8 nodes, MLP archives with a high degree of accuracy (≥ 0.94). In conclusion, by extracting features from multiple sensors and utilizing an MLP classifier, it is possible to maintain a high degree of accuracy when classifying gait cycle phases. Phase classification may be useful for detecting periodic phases of the gait cycle in both healthy volunteers and Parkinson’s disease patients.

Climbing stairs is a fundamental part of daily life, adding additional demands on the postural control system compared to level walking. Although real-world gait analysis studies likely contain stair ambulation sequences, algorithms dedicated to the analysis of such activities are still missing. Therefore, we propose a new gait analysis pipeline for foot-worn inertial sensors, which can segment, parametrize, and classify strides from continuous gait sequences that include level walking, stair ascending, and stair descending. For segmentation, an existing approach based on the hidden Markov model and a feature-based gait event detection were extended, reaching an average segmentation F1 score of 98.5% and gait event timing errors below ±10 for all conditions. Stride types were classified with an accuracy of 98.2% using spatial features derived from a Kalman filter-based trajectory reconstruction. The evaluation was performed on a dataset of 20 healthy participants walking on three different staircases at different speeds. The entire pipeline was additionally validated end-to-end on an independent dataset of 13 Parkinson’s disease patients. The presented work aims to extend real-world gait analysis by including stair ambulation parameters in order to gain new insights into mobility impairments that can be linked to clinically relevant conditions such as a patient’s fall risk and disease state or progression.

Gait analysis is the study of human movements by analyzing temporal and spatial gait features. Research has shown that Parkinson’s disease can degenerate human mobility, thereby causing afflicted individuals to behave differently in terms of gait characteristics. In this work, we propose an optimized method that assists us in better distinguishing people with Parkinson’s disease from normal subjects. The spatial-temporal gait features are extracted by using a real U-shaped pressure-sensitive gait-sensing walkway. After pre-processing optimizations, including nondimensionalization and normalization of the raw features, we feed the features to an SVM classifier for training. The Particle Swarm Optimization algorithm is adopted to optimize the classification model. Experimental results show that the optimized method outperforms its predecessor by improving the accuracy from 87.12% to 95.66%, which shows the effectiveness of our proposed method in detecting Parkinson’s Disease patients.

Conditions such as Parkinson's disease (PD), a chronic neurodegenerative disorder which severely affects the motor system, will be an increasingly common problem for our growing and aging population. Gait analysis is widely used as a noninvasive method for PD diagnosis and assessment. However, current clinical systems for gait analysis usually require highly specialized cameras and lab settings, which are expensive and not scalable. This paper presents a computer vision-based gait analysis system using a camera on a common mobile phone. A simple PVC mat was designed with markers printed on it, on which a subject can walk whilst being recorded by a mobile phone camera. A set of video analysis methods were developed to segment the walking video, detect the mat and feet locations, and calculate gait parameters such as stride length. Experiments showed that stride length measurement has a mean absolute error of 0.62 cm, which is comparable with the "gold standard" walking mat system GAITRite. We also tested our system on Parkinson's disease patients in a real clinical environment. Our system is affordable, portable, and scalable, indicating a potential clinical gait measurement tool for use in both hospitals and the homes of patients.

PurposeParkinson’s Disease comes on top among neurodegenerative diseases affecting 10 million worldwide. To detect Parkinson’s Disease in a prior state, gait analysis is an effective choice. However, monitoring of Parkinson’s Disease using gait analysis is time consuming and exhaustive for patients and physicians. To assess severity of symptoms, a rating scale called Unified Parkinson's Disease Rating Scale is used. It determines mild and severe cases. Today, Parkinson’s Disease severity assessment is made in gait laboratories and by manual examination. These are time consuming and it is costly for health institutions to build and maintain laboratories. By using low-cost wearables and an effective model, aforementioned problems can be solved.MethodsWe provide a computerized solution for quantifiable assessment of Parkinson’s Disease symptoms severity. By using wearable sensors, our framework can predict exact symptom values to assess Parkinson’s Disease severity. We propose a deep learning approach that utilizes Ground Reaction Force sensors. From sensor signals, features are extracted and fed to a hybrid deep learning model. This model is the combination of Convolutional Neural Networks and Locally Weighted Random Forest.ResultsProposed framework achieved 0.897, 3.009, 4.556 in terms of Correlation Coefficient, Mean Absolute Error and Root Mean Square Error, respectively. Proposed framework outperformed other machine and deep learning models. We also evaluated classification performance for disease detection. We outperformed most of the previous studies, achieving 99.5% accuracy, 98.7% sensitivity and 99.1% specificity.ConclusionThis is the first study to use a deep learning regression approach to predict exact symptom value of Parkinson’s Disease patients. Results show that this approach can be effectively employed as a disease severity assessment tool using wearable sensors.

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. We searched PubMed for studies published between 1 January 2005, and 30 August 2019 on gait analysis in PD. 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. Only those studies that reported at least 80% sensitivity and specificity were included. 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%. 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%. 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. In addition, none of the reported algorithms and technologies has been validated in large scale, independent studies.