Validation of plantar pressure simulations using finite and discrete element modelling in healthy and diabetic subjects

Machine learning assisted classification between diabetic polyneuropathy and healthy subjects using plantar pressure and temperature data: a feasibility study

Automated and early detection of diabetics with polyneuropathy in an ambulatory health monitoring setup may reduce the major risk factors for diabetic patients. Increased and localized plantar pressure associated with impaired pain and temperature is a combination of developing foot ulcers in subjects with polyneuropathy. Although many interesting research works have been reported in this area, most of them emphasize on signal acquisition process and plantar pressure distribution in the foot region. In this work, a machine learning assisted low complexity technique was developed using plantar pressure and temperature signals which will classify between diabetic polyneuropathy and healthy subjects. Principal component analysis (PCA) and maximum relevance minimum redundancy (mRMR) methods were used for feature extraction and selection respectively followed by k-NN classifier for binary classification. The proposed technique was evaluated with 100 min of publicly available annotated data from 43 subjects and provides blind test accuracy, sensitivity, precision, F1-score, and area under curve (AUC) of 99.58%, 99.50%, 99.44%, 99.47% and 99.56% respectively. A low resource hardware implementation in ARM v6 controller required an average memory usage of 81.2 kB and latency of 1.31 s to process 9 s pressure and temperature data collected from 16 sensor channels for each of the foot region.

Relationship between Plantar Pressure and Sensory Disturbance in Patients with Hansen's Disease-Preliminary Research and Review of the Literature

Orthoses and insoles are among the primary treatments and prevention methods of refractory plantar ulcers in patients with Hansen’s disease. While dynamic plantar pressure and tactile sensory disturbance are the critical pathological factors, few studies have investigated whether a relationship exists between these two factors. In this study, dynamic pressure measured using F-scan system and tactile sensory threshold evaluated with monofilament testing were determined for 12 areas of 20 feet in patients with chronic Hansen’s disease. The correlation between these two factors was calculated for each foot, for each clinical category of the foot (0–IV) and across all feet. A significant correlation was found between dynamic pressure and tactile sensation in Category II feet (n = 8, p = 0.016, r² = 0.246, Spearman’s rank test). In contrast, no significant correlation was detected for the entire foot or within the subgroups for the remainder of the clinical categories. However, the clinical manifestation of lesion areas showed high variability: (1) pressure concentrated, sensation lost; (2) margin of pressure concentration, sensation lost; (3) pressure concentrated, sensation severely disturbed but not lost; and (4) tip of the toe. These results may indicate that, even though there was a weak relationship between dynamic pressure and tactile sensation, it is important to assess both, in addition to the basics of orthotic treatment in patients with Hansen’s disease presenting with refractory plantar ulceration.

In Silico-Enhanced Treatment and Rehabilitation Planning for Patients with Musculoskeletal Disorders: Can Musculoskeletal Modelling and Dynamic Simulations Really Impact Current Clinical Practice?

Over the past decades, the use of computational physics-based models representative of the musculoskeletal (MSK) system has become increasingly popular in many fields of clinically driven research, locomotor rehabilitation in particular. These models have been applied to various functional impairments given their ability to estimate parameters which cannot be readily measured in vivo but are of interest to clinicians. The use of MSK modelling and simulations allows analysis of relevant MSK biomarkers such as muscle and joint contact loading at a number of different stages in the clinical treatment pathway in order to benefit patient functional outcome. Applications of these methods include optimisation of rehabilitation programs, patient stratification, disease characterisation, surgical pre-planning, and assistive device and exoskeleton design and optimisation. This review provides an overview of current approaches, the components of standard MSK models, applications, limitations, and assumptions of these modelling and simulation methods, and finally proposes a future direction.