Detecting Freezing Of Gait Episodes Research Articles

A Novel Physical Mobility Task to Assess Freezers in Parkinson's Disease.

Freezing of gait (FOG), one of the most disabling features of Parkinson's disease (PD), is a brief episodic absence or marked reduction in stride progression despite the intention to walk. Progressively more people who experience FOG restrict their walking and reduce their level of physical activity. The purpose of this study is to develop and validate a physical mobility task that induces freezing of gait in a controlled environment, employing known triggers of FOG episodes according to the literature. To validate the physical mobility tasks, we recruited 10 volunteers that suffered PD-associated freezing (60.6 ± 7.29 years-old) with new FOG-Q ranging from 12 to 26. The validation of the proposed method was carried out using inertial sensors and video recordings. All subjects were assessed during the OFF and ON medication states. The total number of FOG occurrences during data collection was 144. The proposed tasks were able to trigger 120 FOG episodes, while the TUG test caused 24. The Inertial Measurement Unit (IMU) with accelerometer and gyroscope could not only detect FOG episodes but also allowed us to visualize the three types of FOG: akinesia, festination and trembling in place.

Evaluation of Gait Parameters using Wearable Sensors in Simulated Freezing of Gait Episodes

Background and Hypothesis: Freezing of gait (FoG), the failure to initiate or maintain locomotion, is a common symptom in Parkinsonism that severely impacts patients’ quality of life. Wearable technology such as inertial measurement units (IMU) are being widely investigated as a method of detecting these episodes with increasing sensitivity and specificity. The aim of this initial study is to collect objective gait data using IMU sensors via Perception Neuron software and to develop an analysis pipeline for quick in-clinic calculation of gait parameters.
 Experimental Design: 16 IMU’s were attached to the feet, legs, arms, waist, back, and head in individuals without any gait abnormalities. Subjects were instructed to walk 10 meters normally, followed by 10 meters with interspersed, simulated FoG episodes. This data was processed and analyzed through Axis Studio and MATLAB to identify kinematic markers associated with gait. The correlation between the right and left foot gyroscopic data in the z-axis was determined and plotted against time, with any correlation less than 0.5 being considered a FoG episode. Further, parameters of gait including stride duration, stance duration, swing duration, and stride length were calculated from input of the raw data to establish a control comparison for future studies with Parkinson’s patients.
 Results: Data analysis demonstrated increased stride duration, stance duration and swing duration with shorter stride length in the stimulated FoG episodes as compared to normal gait. The developed MATLAB code was further able to accurately distinguish the simulated FoG episodes from normal gait given the correlation parameter difference of <0.5.
 Conclusion: Our findings showed that data obtained from Perception Neuron IMU can be processed to succinctly detect FoG episodes and calculate gait parameters. Ultimately, this data will be used as the baseline for a future study investigating the use of spinal cord stimulation as treatment for FoG in individuals with Parkinson’s Disease.

Detection of Freezing of Gait Episodes in Patients with Parkinson's Disease Using Electroencephalography and Motion Sensors

Objective: Freezing of gait (FOG) is an important concern for both patients with Parkinson's disease (pwPD) and physicians. In this study, we aimed to introduce a study protocol and our initial data. The data were subsequently used in machine learning models to detect FOG episodes using brain activity signals and motion data in the laboratory setting using complex FOG-evoking activities in a sample of pwPD with and without FOG compared with age-matched healthy controls. Subjects and Methods: An experimental task to evoke a FOG episode was designed. This experimental task was tested on two pwPD with FOG in “on” and “off” periods and one healthy control. Brain activity signals and motion data were collected simultaneously using electroencephalography (EEG) and inertial measurement units (IMUs). Results: The whole procedure took about 2 h, during which around 30 min were spent on walking tasks, involving 35 complete tours in the designed 8-m hallway by pwPD. Both EEG and IMUs sensor data could be collected, accompanied by FOG episode data marked by the neurologist. The video recordings of the patient's walking tasks were checked and reanalyzed by the neurologist sometime after the data experiment for marking the beginnings and ends of the observed FOG episodes more precisely. In the end, 24 stops were marked as FOG, which corresponded to 11% of the sensor data collected during the walking tasks. Conclusion: The designed FOG-evoking task protocol could be performed without any adverse effects, and it created enough FOG episodes for analysis. EEG and motion sensor data could be successfully collected without any significant artifacts.

DeepFoG: An IMU-Based Detection of Freezing of Gait Episodes in Parkinson's Disease Patients via Deep Learning.

Freezing of Gait (FoG) is a movement disorder that mostly appears in the late stages of Parkinson’s Disease (PD). It causes incapability of walking, despite the PD patient’s intention, resulting in loss of coordination that increases the risk of falls and injuries and severely affects the PD patient’s quality of life. Stress, emotional stimulus, and multitasking have been encountered to be associated with the appearance of FoG episodes, while the patient’s functionality and self-confidence are constantly deteriorating. This study suggests a non-invasive method for detecting FoG episodes, by analyzing inertial measurement unit (IMU) data. Specifically, accelerometer and gyroscope data from 11 PD subjects, as captured from a single wrist-worn IMU sensor during continuous walking, are processed via Deep Learning for window-based detection of the FoG events. The proposed approach, namely DeepFoG, was evaluated in a Leave-One-Subject-Out (LOSO) cross-validation (CV) and 10-fold CV fashion schemes against its ability to correctly estimate the existence or not of a FoG episode at each data window. Experimental results have shown that DeepFoG performs satisfactorily, as it achieves 83%/88% and 86%/90% sensitivity/specificity, for LOSO CV and 10-fold CV schemes, respectively. The promising performance of the proposed DeepFoG reveals the potentiality of single-arm IMU-based real-time FoG detection that could guide effective interventions via stimuli, such as rhythmic auditory stimulation (RAS) and hand vibration. In this way, DeepFoG may scaffold the elimination of risk of falls in PD patients, sustaining their quality of life in everyday living activities.

Foot Pressure Wearable Sensors for Freezing of Gait Detection in Parkinson's Disease.

Freezing of Gait (FoG) is a common symptom in Parkinson’s Disease (PD) occurring with significant variability and severity and is associated with increased risk of falls. FoG detection in everyday life is not trivial, particularly in patients manifesting the symptom only in specific conditions. Various wearable devices have been proposed to detect PD symptoms, primarily based on inertial sensors. We here report the results of the validation of a novel system based on a pair of pressure insoles equipped with a 3D accelerometer to detect FoG episodes. Twenty PD patients attended a motor assessment protocol organized into eight multiple video recorded sessions, both in clinical and ecological settings and both in the ON and OFF state. We compared the FoG episodes detected using the processed data gathered from the insoles with those tagged by a clinician on video recordings. The algorithm correctly detected 90% of the episodes. The false positive rate was 6% and the false negative rate 4%. The algorithm reliably detects freezing of gait in clinical settings while performing ecological tasks. This result is promising for freezing of gait detection in everyday life via wearable instrumented insoles that can be integrated into a more complex system for comprehensive motor symptom monitoring in PD.

Inertial Sensor Based Detection of Freezing of Gait for On-Demand Cueing in Parkinson’s Disease

Freezing of Gait (FoG) is one of the cardinal symptoms of Parkinson’s disease, which arises in the late stages of the disease. It affects the gait cycle and increases the risk of falling. FoG leads to heterogeneous gait cycles, which makes the detection of gait phases and events difficult. In this article, we introduce a new inertial measurement unit-based approach for detecting Parkinsonian gait phases based on the acceleration, velocity, rate of turn and orientation of the foot. Furthermore, we introduce a new gait evaluation measurement, the so-called GaitScore, for distinguishing between normal and FoG-affected motion phases and thus for detecting FoG episodes. Preliminary results show that the extreme values of the pitch angle during a motion phase provide valuable information for the detection of FoG. The proposed method can detect FoG episodes with a sensitivity of 97% and specificity of 87%. The reference data were generated by clinical experts who annotated FoG episodes in video data synchronized with the measurements of the inertial sensors. The detection of FoG in real-time enables on-demand cueing.

Prediction of Freezing of Gait in Parkinson's Disease Using Statistical Inference and Lower-Limb Acceleration Data.

The freezing of gait (FoG) is a common type of motor dysfunction in advanced Parkinson's disease (PD) associated with falls. Over the last decade, a significant amount of studies has been focused on detecting FoG episodes in clinical and home environments. Yet, there remains a paucity of techniques regarding real-time prediction of FoG before its occurrence. In this paper, a new algorithm was employed to define the best combination of sensor position, axis, sampling window length, and features to predict FoG. We hypothesized that gait deterioration before FoG onsets can be discriminated from normal gait using statistical analysis of features from successive windows of data collected from lower-limb accelerometers. We defined a new performance measure, "predictivity", to compare the number of correctly predicted FoG events among different combinations. We characterized the system performance using data from 10 PD patients, who experienced FoG while performing several walking tasks in a lab environment. The analysis of 120 different combinations revealed that prediction of FoG can be realized by using an individual shank sensor and sample entropy calculated from the horizontal forward axis with window length of 2 s (88.8%, 92.5%, and 89.0% for average predictivity, sensitivity, and specificity, respectively).

Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit

Among Parkinson’s disease (PD) motor symptoms, freezing of gait (FOG) may be the most incapacitating. FOG episodes may result in falls and reduce patients’ quality of life. Accurate assessment of FOG would provide objective information to neurologists about the patient’s condition and the symptom’s characteristics, while it could enable non-pharmacologic support based on rhythmic cues.This paper is, to the best of our knowledge, the first study to propose a deep learning method for detecting FOG episodes in PD patients. This model is trained using a novel spectral data representation strategy which considers information from both the previous and current signal windows. Our approach was evaluated using data collected by a waist-placed inertial measurement unit from 21 PD patients who manifested FOG episodes. These data were also employed to reproduce the state-of-the-art methodologies, which served to perform a comparative study to our FOG monitoring system.The results of this study demonstrate that our approach successfully outperforms the state-of-the-art methods for automatic FOG detection. Precisely, the deep learning model achieved 90% for the geometric mean between sensitivity and specificity, whereas the state-of-the-art methods were unable to surpass the 83% for the same metric.

Detection of freezing of gait for Parkinson’s disease patients with multi-sensor device and Gaussian neural networks

Freezing of Gait (FoG) in Parkinson Disease (PD) is a sudden episode characterized by a brief failure to walk. The aim of this study is to detect FoG episodes using a multi-sensor device for data acquisition, and Gaussian neural networks as a classification tool. Thus we have built a multi sensor prototype that detects FoG using new indicators like the variation of the inter-foot distance or the knee angle. Data are acquired from PD patients having FoG as a major symptom. The major social challenge is obtaining the acknowledgment of patients to participate in our study, whereas the main technical difficulty is extracting efficient features from various walking behaviors. For that purpose, the acquired signals are analyzed in order to extract both time and frequency domain features that separate the FoG class from the other gaits modes. Due to the complexity of FoG episodes, the optimal features are then extracted using Principal Component Analysis technique. Another contribution is to introduce the combined data into the Gaussian Neural Network (GNN) classification method, that is a new technique used for FoG detection, and has been developed in our previous works. Moreover, the classical thresholding method is implemented to compare and validate the GNN method. Results showed the feasibility of integrating the chosen sensors, in addition to the effectiveness of combining data from different types of sensors on the classification rate. The efficiency rate of classification in the proposed method is about 87 %.

Motion analysis of real-time somatosensory cue on freezing of gait during turning in people with Parkinson's disease

Background: Freezing of gait (FOG) has a substantial impact on balance and walking in people with Parkinson’s disease (PD). Previous studies have shown that insufficient weight shift to unload the swinging leg may contribute to gait discontinuation. Purpose: This study aimed to examine the immediate effect of a real-time somatosensory cue to facilitate lateral weight shift during FOG in people with PD. Methods:Wedesigned awearable cue devicewhich composedof a sensor part and stimulator part. The sensor part is an inertial sensor placed at shank, to detect FOG episodes with the embedded time-frequency analysis algorithm. The stimulator part is a vibrator pad placed below the lateral malleolus, to facilitate lateral weight shift during FOG. Eighteen idiopathic people with PD (Age: 68.4± 4.2 year) having FOG were recruited, and took this study during their medication OFFperiod. Participants conducted the timedUp-and-Go test with the stimulator part turned on (cued trials) and off (nocue trials). Participants were videotaped and the clips were reviewed by two physical therapists to identify the freezing episodes. Weight shift was evaluated by the inclination angle of the joint line of center of mass and center of pressure. FOG severity was evaluated by FOG number and FOG duration during turning. Turning performance was evaluated by turning time and turning steps. Results: The sensor part showed moderate-to-high accuracy in detecting FOG episodes with respect to therapists’ visual inspection (sensitivity: 0.78; specificity: 0.88). Compared with no-cue trials, participants in the cued trials had greater frontal inclination angle during FOG (cued: 8.17± 0.98 vs. no-cue: 7.26± 0.53, degree). In addition, the cued trials had decreased FOG duration (cued: 2.12 vs. nocue: 3.62, second) and turning time (cued: 5.49± 2.48 vs. no-cue: 8.67± 3.70, second) than the no-cue trials, despite no difference in turning steps (cued: 10.6± 4.2 vs. no-cue: 12.2± 5.0, steps) and FOG number (cued: 0.8 vs. no-cue: 1.1, FOG per turning). Conclusion(s): Our results showed that the real-time somatosensory cue could help gait reinitiation by facilitating lateral weight shift during FOG. The shortened FOGduration decreased the turning time in people with PD. Implications: A wearable device with inertial sensor is capable of detecting FOG episodes and controlling realtime somatosensory cue to facilitate weight shift. This could provide a potential intervention for FOG during daily activities in people with PD.