Wearable Devices for Gait Analysis in Intelligent Healthcare

In this study, we review the role of wearable devices in tracking our daily locomotion. We discuss types of wearable devices that can be used, methods for gait analyses, and multiple healthcare-related applications aided by artificial intelligence. Impaired walking and locomotion are common resulting from injuries, degenerative pathologies, musculoskeletal disorders, and various neurological damages. Daily tracking and gait analysis are convenient and efficient approaches for monitoring human walking, where concreate and rich data can be obtained for examining our posture control mechanism during body movement and providing enhanced clinical pieces of evidence for diagnoses and treatments. Many sensors in wearable devices can help to record data of walking and running; spatiotemporal and kinematic variables can be further calculated in gait analysis. We report our previous works in gait analysis, discussing applications of wearable devices for detecting foot and ankle lesions, supporting surgeons in early diagnosis, and helping physicians with rehabilitation.

Mediolateral stability index as a biomarker for Parkinson's disease progression: A graph connectivity based approach

This paper presents a novel metric to serve as a bio-marker for understanding and detecting progression of Parkinson's disease (PD). Proposed metric, termed as `Mediolateral Stability (MLS) index' has been derived from processing insole gait data using graph connectivity analysis. The proposed metric focuses on variability of mediolateral pressure in foot during gait progression. Vertical Ground Reaction Force (VGRF) and stride time information derived from a wearable insole for PD subjects as well as healthy controls are processed to create a connectivity graph. The insole contains eight pressure sensitive sensors for each foot and these sensors serve as the nodes of the connectivity graph. The proposed MLS index shows significant difference (p <; 0.05) in between control and PD groups and also in between progressive stages of PD, such as mild and moderate PD groups with p <; 0.05. Proposed graph connectivity based feature can act as a bio marker to correctly classify PD, identify early onset of PD and trace changes due to disease progression and can also provide information about dynamic pressure distribution during gait.

PI-Sole: A Low-Cost Solution for Gait Monitoring Using Off-The-Shelf Piezoelectric Sensors and IMU

In this paper, viability of low-cost off-the-shelf Piezoelectric ceramic disc elements is explored for an insole-based gait monitoring system, `PI-Sole' (Piezo In-Sole). Piezoelectric elements can sense dynamic changes in pressure in a closed-loop environment with good sensitivity and a wide measurement range. In this paper, method to enable these elements to continuously sense plantar pressure while walking is proposed, making them a very cost-efficient alternative to the widely used Force Sensing Resistors (FSR) and pressure plates for monitoring human gait. However, piezoelectric elements show hysteresis in their force response, inducing a drift in calculated pressure which increases with time. A novel and effective method to perform detrending of the signal is also presented utilizing stride contexts from a 6-DoF Inertial Measurement Unit (IMU) and the same is utilized to perform zero-correction in the pressure data. 3-D trajectories of strides are calculated using the IMU, and parameters like stride length, stride height etc. are further derived. In order to test the validity of our proposed methods, important kinetic parameters like Vertical Ground Reaction Force (VGRF) and Center of Pressure (CoP) are calculated using PI-Sole and compared to the ones calculated using FSR's in multiple prior works. Applicability of PI-Sole is demonstrated further by depicting and analysing characteristic differences between a heel-strike toe-off stance type, and a flat-strike stance type, the latter being one of the primary symptoms in many cases of pathological gait, including Parkinsonian gait. Important artefacts from foot's height profile while walking are analysed for both stance types in context of standard gait events. We report a mean error of 2.8cm in stride length calculation, and a mean accuracy of 94.5% in calculating swing/stance duration of gait cycles.

A Dual-Modal Attention-Enhanced Deep Learning Network for Quantification of Parkinson's Disease Characteristics

It is well known that most patients with Parkinson's disease (PD) have different degree of movement disorders, such as shuffling, festination and akinetic episodes, which could degenerate the life quality of PD patients. Therefore, it is very useful to develop a computerized tool to provide an objective evaluation of PD patients' gait. In this study, we implemented a novel gait evaluating approach to provide not only a binary classification of PD gaits and normal walking, but also a quantification of the PD gaits to relate them to the PD severity level. The proposed system is a dual-modal deep-learning-based model, where left and right gait is modeled separately by a convolutional neural network (CNN) followed by an attention-enhanced long short-term memory (LSTM) network. The left and right samples for model training and testing were segmented sequentially from multiple 1D vertical ground reaction force (VGRF) signals according to the detected gait cycle. Experimental results indicate that our model can provide state-of-the-art performance in terms of classification accuracy. It is expected that the proposed model can be a useful gait assistance to provide a quantitative evaluation of PD gaits with high confidence and accuracy if trained suitably.

A convolutional neural network Cascade for plantar pressure images registration

BACKGROUND

Plantar pressure image (PPI) recorded in high spatial and temporal resolution is very useful in clinical gait analysis. For functional analysis of PPI, image registration is often performed to maximally correlate source image with a template image. Previous methods estimate the registration parameters by iteratively optimizing different objective functions. These methods are often computational expensive to achieve satisfactory registration accuracy.

RESEARCH QUESTION

Can we develop a single PPI registration technique that performs more rapidly than previous methods, and that also maintains adequate PPI correspondence as defined by various (dis)similarity metrics?

METHODS

A cascaded convolutional neural network (CNN) was proposed for the registration of PPIs. Our model was trained to learn a regression from the difference between the template and misaligned images to the registration parameters. The registration performance was evaluated by three different metrics, i.e. the mean squared error (MSE), the exclusive or (XOR), and the mutual information (MI). For comparison, four previous methods were also implemented. These included the principal axes (PA) method, the center of pressure trajectory (COP) method, the MSE method, and the XOR method.

RESULTS

Experimental results on a dataset with 71 PPI template-source pairs showed that the proposed CNN-based method could obtain comparable registration accuracy to the MSE and XOR method. With regards to the registration speed, registration durations (mean ± sd in seconds) per image pair were: MSE (30.584 ± 2.171), XOR (24.245 ± 1.596), PA (0.016 ± 0.003), COP (25.614 ± 0.341), and the proposed model (0.054 ± 0.007).

SIGNIFICANCE

Our findings indicate that the proposed registration approach can achieve high accuracy but less computational time. Thus, it is more practical to utilize our pre-trained CNN-based model to develop near-real time applications for plantar pressure images registration.

Network physiology of 'fight or flight' response in facial superficial blood vessels

OBJECTIVE

We introduced a novel framework to identify the dynamic pattern of blood flow changes in the cutaneous superficial blood vessels of the face for 'fight or flight' responses through facial thermal imaging.

APPROACH

For this purpose, a thermal dataset was collected from 41 subjects in a mock crime scenario. Five facial areas including periorbital, forehead, perinasal, cheek and chin were selected on the face. Due to the cause and effect movement of blood in the facial cutaneous vasculature, the effective connectivity approach and graph analysis were used to extract causality features. The effective connectivity was quantified using a modified version of the multivariate Granger causality (GC) method among each pair of facial region of interests.

MAIN RESULTS

Validation was performed using statistical analysis, and the results demonstrated that the proposed method was statistically significant in detecting the physiological pattern of deceptive anxiety on the face. Moreover, the obtained graph is visualized by different schemes to show these interactions more effectively. We used machine learning techniques to classify our data based on the GC values, which result in a greater than 87% accuracy rate in discriminating between deceptive and truthful subjects.