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

Abstract

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.

Wearable Sensor for Assessing Gait and Postural Alterations in Patients with Diabetes: A Scoping Review

“…Thus, they are useful for implementing strategies to reduce the risk of falls which represents a major public health problem [50]. Recent reports suggest that IMUs, besides being useful in evaluating and to monitoring gait alterations in patients with neurodegenerative diseases such as Parkinson's disease [51][52][53], are useful for; patients with osteoarthritis [54], with benign paroxysmal positional vertigo, the most common peripheral vestibular disorder, leading to balance difficulties and increased fall risks [55] and walking disturbances in sarcopenic patients [56], they are also useful for gait event detection and analysis of gait alterations in patients with diabetes secondary to DPN [23,24,[27][28][29]31]. In spatiotemporal gait parameters recorded using a wearable sensor in patients with DPN, Kang et al showed that gait initiation steps and dynamic balance may be more sensitive than gait speed for detecting gait deterioration due to DPN [23], and Najafi et al demonstrated that gait alteration in patients with DPN is most pronounced while walking barefoot over longer distances [31].…”

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

“…Data were labeled as Non-FOG, Pre-FOG, or FOG. Pre-FOG was defined as 2 s of data before each FOG, as in [ 54 , 55 ]. The labeled data were divided into 1 s windows with 0.2 s shift between windows (i.e., 80% overlap) ( Fig 3 ).…”

“…Ten features were extracted from each window. The features were calculated from lower-limb IMU and plantar pressure data and were selected by Relief-F feature ranking from among over 850 total features, described in [ 54 ]. The features used were the dominant fast Fourier transform (FFT) frequency of foot centre of pressure (COP) velocity in medial/lateral (ML) directions for the right leg and anterior/posterior (AP) for the right and left legs, the dominant FFT frequency of thigh accelerometers in the AP direction for the right and left legs, mean energy of wavelet transform (WT) approximation coefficient of COP position in the AP direction for the right leg, number of COP AP reversals for the right and left legs, mean of WT approximation coefficient for COP position in AP for the right leg, and the min of the COP detail coefficient in the AP direction for the right leg.…”

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

“…Plantar pressure has been used in rehabilitation strategies [ 23 – 26 ], fall-risk prediction [ 27 ] and classification [ 28 ] in older adults, and classifying gait as PD or healthy control [ 29 ]. Only recently, plantar pressure data has been used together with accelerometer data for FOG detection [ 30 ], and in our recent research for early FOG detection and prediction (together with IMU data [ 31 , 32 ] and alone [ 32 , 33 ]). Plantar pressure data was useful in detecting FOG and the transition from normal walking into a freeze.…”