Prediction of Freezing of Gait in Parkinson's Disease Using Unilateral and Bilateral Plantar-Pressure Data.

Background: Freezing of gait (FOG) is an intermittent walking disturbance experienced by people with Parkinson's disease (PD). FOG has been linked to falling, injury, and overall reduced mobility. Wearable sensor-based devices can detect freezes already in progress and provide a cue to help the person resume walking. While this is helpful, predicting FOG episodes before onset and providing a timely cue may prevent the freeze from occurring. Wearable sensors mounted on various body parts have been used to develop FOG prediction systems. Despite the known asymmetry of PD motor symptom manifestation, the difference between the most affected side (MAS) and least affected side (LAS) is rarely considered in FOG detection and prediction studies. Methods: To examine the effect of using data from the MAS, LAS, or both limbs for FOG prediction, plantar pressure data were collected during a series of walking trials and used to extract time and frequency-based features. Three datasets were created using plantar pressure data from the MAS, LAS, and both sides together. ReliefF feature selection was performed. FOG prediction models were trained using the top 5, 10, 15, 20, 25, or 30 features for each dataset. Results: The best models were the MAS model with 15 features and the LAS and bilateral models with 5 features. The LAS model had the highest sensitivity (79.5%) and identified the highest percentage of FOG episodes (94.9%). The MAS model achieved the highest specificity (84.9%) and lowest false positive rate (1.9 false positives/walking trial). Overall, the bilateral model was best with 77.3% sensitivity and 82.9% specificity. In addition, the bilateral model identified 94.2% of FOG episodes an average of 0.8 s before FOG onset. Compared to the bilateral model, the LAS model had a higher false positive rate; however, the bilateral and LAS models were similar in all the other evaluation metrics. Conclusion: The LAS model would have similar FOG prediction performance to the bilateral model at the cost of slightly more false positives. Given the advantages of single sensor systems, the increased false positive rate may be acceptable to people with PD. Therefore, a single plantar pressure sensor placed on the LAS could be used to develop a FOG prediction system and produce performance similar to a bilateral system.

Prediction and detection of freezing of gait in Parkinson's disease from plantar pressure data using long short-term memory neural-networks.

BACKGROUND: Freezing of gait (FOG) is a walking disturbance in advanced stage Parkinson's disease (PD) that has been associated with increased fall risk and decreased quality of life. Freezing episodes can be mitigated or prevented with external intervention such as visual or auditory cues, activated by FOG prediction and detection systems. While most research on FOG detection and prediction has been based on inertial measurement unit (IMU) and accelerometer data, plantar-pressure data may capture subtle weight shifts unique to FOG episodes. Different machine learning algorithms have been used for FOG detection and prediction; however, long short-term memory (LSTM) deep learning methods hold an advantage when dealing with time-series data, such as sensor data. This research aimed to determine if LSTM can be used to detect and predict FOG from plantar pressure data alone, specifically for use in a real-time wearable system. METHODS: Plantar pressure data were collected from pressure-sensing insole sensors worn by 11 participants with PD as they walked a predefined freeze-provoking path. FOG instances were labelled, 16 features were extracted, and the dataset was balanced and normalized (z-score). The resulting datasets were classified using long short-term memory neural-network models. Separate models were trained for detection and prediction. For prediction models, data before FOG were included in the target class. Leave-one-freezer-out cross validation was used for model evaluation. In addition, the models were tested on all non-freezer data to determine model specificity. RESULTS: The best FOG detection model had 82.1% (SD 6.2%) mean sensitivity and 89.5% (SD 3.6%) mean specificity for one-freezer-held-out cross validation. Specificity improved to 93.3% (SD 4.0%) when ignoring inactive state data (standing) and analyzing the model only on active states (turning and walking). The model correctly detected 95% of freeze episodes. The best FOG prediction method achieved 72.5% (SD 13.6%) mean sensitivity and 81.2% (SD 6.8%) mean specificity for one-freezer-held-out cross validation. CONCLUSIONS: Based on FOG data collected in a laboratory, the results suggest that plantar pressure data can be used for FOG detection and prediction. However, further research is required to improve FOG prediction performance, including training with a larger sample of people who experience FOG.

Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson's disease.

Freezing of gait (FOG) is an intermittent walking disturbance experienced by people with Parkinson's disease (PD). Wearable FOG identification systems can improve gait and reduce the risk of falling due to FOG by detecting FOG in real-time and providing a cue to reduce freeze duration. However, FOG prediction and prevention is desirable. Datasets used to train machine learning models often generate ground truth FOG labels based on visual observation of specific lower limb movements (event-based definition) or an overall inability to walk effectively (period of gait disruption based definition). FOG definition ambiguity may affect model performance, especially with respect to multiple FOG in rapid succession. This research examined whether merging multiple freezes that occurred in rapid succession could improve FOG detection and prediction model performance. Plantar pressure and lower limb acceleration data were used to extract a feature set and train decision tree ensembles. FOG was labeled using an event-based definition. Additional datasets were then produced by merging FOG that occurred in rapid succession. A merging threshold was introduced where FOG that were separated by less than the merging threshold were merged into one episode. FOG detection and prediction models were trained for merging thresholds of 0, 1, 2, and 3 s. Merging slightly improved FOG detection model performance; however, for the prediction model, merging resulted in slightly later FOG identification and lower precision. FOG prediction models may benefit from using event-based FOG definitions and avoiding merging multiple FOG in rapid succession.

Early Detection of Freezing of Gait during Walking Using Inertial Measurement Unit and Plantar Pressure Distribution Data.

Freezing of gait (FOG) is a sudden and highly disruptive gait dysfunction that appears in mid to late-stage Parkinson's disease (PD) and can lead to falling and injury. A system that predicts freezing before it occurs or detects freezing immediately after onset would generate an opportunity for FOG prevention or mitigation and thus enhance safe mobility and quality of life. This research used accelerometer, gyroscope, and plantar pressure sensors to extract 861 features from walking data collected from 11 people with FOG. Minimum-redundancy maximum-relevance and Relief-F feature selection were performed prior to training boosted ensembles of decision trees. The binary classification models identified Total-FOG or No FOG states, wherein the Total-FOG class included data windows from 2 s before the FOG onset until the end of the FOG episode. Three feature sets were compared: plantar pressure, inertial measurement unit (IMU), and both plantar pressure and IMU features. The plantar-pressure-only model had the greatest sensitivity and the IMU-only model had the greatest specificity. The best overall model used the combination of plantar pressure and IMU features, achieving 76.4% sensitivity and 86.2% specificity. Next, the Total-FOG class components were evaluated individually (i.e., Pre-FOG windows, Freeze windows, transition windows between Pre-FOG and Freeze). The best model detected windows that contained both Pre-FOG and FOG data with 85.2% sensitivity, which is equivalent to detecting FOG less than 1 s after the freeze began. Windows of FOG data were detected with 93.4% sensitivity. The IMU and plantar pressure feature-based model slightly outperformed models that used data from a single sensor type. The model achieved early detection by identifying the transition from Pre-FOG to FOG while maintaining excellent FOG detection performance (93.4% sensitivity). Therefore, if used as part of an intelligent, real-time FOG identification and cueing system, even if the Pre-FOG state were missed, the model would perform well as a freeze detection and cueing system that could improve the mobility and independence of people with PD during their daily activities.

Prediction of Freezing of Gait in Parkinson's Disease from Foot Plantar-Pressure Arrays using a Convolutional Neural Network.

Freezing of gait (FOG) is a sudden cessation of locomotion in advanced Parkinson's disease (PD). A FOG episode can lead to falls, decreased mobility, and decreased overall quality of life. Prediction of FOG episodes provides an opportunity for intervention and freeze prevention. A novel method of FOG prediction that uses foot plantar pressure data acquired during gait was developed and evaluated, with plantar pressure data treated as 2D images and classified using a convolutional neural network (CNN). Data from five people with PD and a history of FOG were collected during walking trials. FOG instances were identified and data preceding each freeze were labeled as Pre-FOG. Left and right foot FScan pressure frames were concatenated into a single 60x42 pressure array. Each frame was considered as an independent image and classified as Pre-FOG, FOG, or Non-FOG, using the CNN. From prediction models using different Pre-FOG durations, shorter Pre-FOG durations performed best, with Pre-FOG class sensitivity 94.3%, and specificity 95.1%. These results demonstrated that foot pressure distribution alone can be a good FOG predictor when treating each plantar pressure frame as a 2D image, and classifying the images using a CNN. Furthermore, the CNN eliminated the need for feature extraction and selection.Clinical Relevance- This research demonstrated that foot plantar pressure data can be used to predict freezing of gait occurrence, using a convolutional neural network deep learning technique. This had the added advantage of eliminating the need for feature extraction and selection.

Selection of Plantar-Pressure and Ankle-Acceleration Features for Freezing of Gait Detection in Parkinson's Disease using Minimum-Redundancy Maximum-Relevance.

Freezing of gait (FOG) is a major hindrance to daily mobility and can lead to falling in people with Parkinson's disease. While wearable accelerometers and gyroscopes have been commonly used for FOG detection, foot plantar pressure distribution could also be considered for this application, given its usefulness in previous gait-based classification. This research examined 325 plantar-pressure based features and 132 acceleration-based features extracted from the walking data of five males with Parkinson's disease who experienced FOG. A set of 61 features calculated from the time domain, Fast Fourier transform (FFT), and wavelet transform (WT) were extracted from multiple input signals; including, total ground reaction force, foot centre of pressure (COP) position, COP velocity, COP acceleration, and 3D ankle acceleration. Minimum-redundancy maximum relevance (mRMR) feature selection was used to rank all features. Plantar-pressure based features accounted for 4 of the top 5 features (ranks 2, 3, 4, 5); the remaining feature was an ankle acceleration based feature (rank 1). The three highest ranked features were the freeze index (calculated from ankle acceleration), total power in the frequency domain (calculated using the FFT from COP velocity), and mean of the WT detail coefficients (calculated from COP velocity). This preliminary analysis demonstrated that features calculated from plantar pressure, specifically COP velocity, performed comparably to ankle acceleration features. Thus, feature sets for FOG detection may benefit from plantar-pressure based features.

Development of an unpowered ankle exoskeleton for walking assist.

Aim: Assistive technologies traditionally rely on either powered actuation or passive structures to provide increased strength, support or the ability to perform specific functions. At one end of the spectrum are powered exoskeletons, which significantly increase a user's strength, but require powerful actuators, complex control systems and heavy power sources. At the other end are orthoses, which are generally unpowered and light in weight, relying on the mechanical properties of passive mechanical elements. Ideally, assistive technologies should combine the benefits of both systems and enhance human motion while remaining lightweight and efficient. This paper presents the development of a lightweight unpowered ankle exoskeleton that relies on the spring-like properties of a Pneumatic Artificial Muscle, which is inflated and sealed.Methods: This flexible air-spring is used to harness gait energy and compliment the human ankle torque at push-off. To mechanically validate the proposed exoskeleton design, a prototype was fabricated and experimentally tested.Results: Unlike other existing devices, the proposed unpowered exoskeleton was able to store a significant amount of energy and release it all at once. The timing mechanism worked as intended and triggered the release of 115 N m of torque when the ankle reached a pre-determined angle.Conclusion: Overall, the device demonstrated the ability to provide significant contribution to the ankle torque, timed to release precisely at the push-off phase of the gait cycle.Implications for RehabilitationThe currently proposed ankle exoskeleton makes use of an unpowered, fully mechanical system to provide walking assistance to users by providing additional torque to the ankle joint.The newly developed assistive device is devised as a solution for persons struggling with mobility issues, and can be used both as a means for rehabilitation or as a permanent assistive devices for patients struggling with long-term disabilities.The device also has potential to be used as a performance enhancing device for ablebodied users by reducing muscle fatigue during extended physical exertion.

Wearable-Sensor-based Detection and Prediction of Freezing of Gait in Parkinson's Disease: A Review.

Freezing of gait (FOG) is a serious gait disturbance, common in mid- and late-stage Parkinson's disease, that affects mobility and increases fall risk. Wearable sensors have been used to detect and predict FOG with the ultimate aim of preventing freezes or reducing their effect using gait monitoring and assistive devices. This review presents and assesses the state of the art of FOG detection and prediction using wearable sensors, with the intention of providing guidance on current knowledge, and identifying knowledge gaps that need to be filled and challenges to be considered in future studies. This review searched the Scopus, PubMed, and Web of Science databases to identify studies that used wearable sensors to detect or predict FOG episodes in Parkinson's disease. Following screening, 74 publications were included, comprising 68 publications detecting FOG, seven predicting FOG, and one in both categories. Details were extracted regarding participants, walking task, sensor type and body location, detection or prediction approach, feature extraction and selection, classification method, and detection and prediction performance. The results showed that increasingly complex machine-learning algorithms combined with diverse feature sets improved FOG detection. The lack of large FOG datasets and highly person-specific FOG manifestation were common challenges. Transfer learning and semi-supervised learning were promising for FOG detection and prediction since they provided person-specific tuning while preserving model generalization.