Automated Peripheral Neuropathy Assessment Using Optical Imaging and Foot Anthropometry

Concurrent Validity of an Automated Footprint Detection Algorithm to Measure Plantar Contact Area During Walking

BACKGROUND

Monitoring footprints during walking can lead to better identification of foot structure and abnormalities. Current techniques for footprint measurements are either static or dynamic, with low resolution. This work presents an approach to monitor the plantar contact area when walking using high-speed videography.

METHODS

Footprint images were collected by asking the participants to walk across a custom-built acrylic walkway with a high-resolution digital camera placed directly underneath the walkway. This study proposes an automated footprint identification algorithm (Automatic Identification Algorithm) to measure the footprint throughout the stance phase of walking. This algorithm used coloration of the plantar tissue that was in contact with the acrylic walkway to distinguish the plantar contact area from other regions of the foot that were not in contact.

RESULTS

The intraclass correlation coefficient (ICC) demonstrated strong agreement between the proposed automated approach and the gold standard manual method (ICC = 0.939). Strong agreement between the two methods also was found for each phase of stance (ICC > 0.78).

CONCLUSIONS

The proposed automated footprint detection technique identified the plantar contact area during walking with strong agreement with a manual gold standard method. This is the first study to demonstrate the concurrent validity of an automated identification algorithm to measure the plantar contact area during walking.

Electronic measurement of plantar contact area during walking using an adaptive thresholding method for Medilogic® pressure-measuring insoles

BACKGROUND

Pressure-measuring insoles have the potential to measure plantar contact area (PA) during walking. However, they are not widely used for this purpose because of the need for a reliable process that can convert the insole output into PA. The purposes of this study were to: (1) develop an adaptive-threshold method for pressure-measuring insoles that can improve the accuracy of the PA measurements during walking, and (2) experimentally assess the accuracy and generalizability of this method.

METHODS

A sample of 42 healthy, ambulatory, young adults (age=24.3±3.2years, mass=67.2±16.9kg, height=1.63±0.08m) completed 10 trials walking on an elevated walkway while wearing Medilogic^® pressure-measuring insoles (sizes 35-45). A total of six insole sizes were considered. Insole data were converted to PA using three unique adaptive-thresholds that were based on percentages of the maximum sum of digital values (MSDV) during an analyzed step. Three values were considered: 0.1%, 0.2%, and 0.3% of the MSDV. Additionally, a fixed-threshold, which is typically used to estimate PA, was assessed. These two techniques, applied to the insole worn on the left foot, were compared with PA obtained from high-resolution reference footprints obtained from optical pedography of the right foot and processed using digital image processing algorithms. An assumption of PA symmetry between the left (insole) and right (barefoot) feet was made and comparisons were conducted over the entire stance phase of walking. The generalizability of the algorithm was assessed by comparing PA errors from insoles with respect to the optical pedography results based on insole size criteria.

RESULTS

The adaptive-thresholds of 0.1%, 0.2%, and 0.3% of MSDV produced mean errors of 7.31±17.44%, -8.62±15.01%, and -20.45±14.18%, respectively. Using the 2-digital value fixed-threshold produced a mean error of 20.88±22.44%. The best performing adaptive-threshold varied among insole sizes.

CONCLUSION

It was observed that the fixed-threshold technique produced large magnitudes of errors. The proposed adaptive-thresholds of 0.1% and 0.2% of the MSDV reduced PA error to ±10% during walking. The adaptive-threshold method consistently reduced PA error vs. the fixed-threshold for each insole size.

The use of a thermal camera and Eulerian enhancement in the examination of pedal pulse and microvascular health

Early detection of impaired blood flow and microvascular functioning is important to prevent ulceration in diabetic patients. This paper aims to first determine if thermal video in conjunction with Eulerian Video Magnification (EVM) can be used to find the pedal pulse rate, and reveal patterns indicative of the foot's microvascular health. Thermal video was captured of a healthy adult's foot while a Doppler ultrasound captured pedal pulse. Another thermal video was captured of a patient's heels. These videos were subjected to EVM, areas of interest were defined and the mean intensity signal was calculated temporally, within each defined area. The healthy adult signals were compared to Doppler data to determine the signal best representative of pedal pulse. The patient signals were examined for patterns. The mean intensity signals best representing pedal pulse in the healthy adult resulted from areas containing an artery close to the skin. The most significant pattern in the patient data was a large difference in signal amplitude from areas containing the left posterior tibial artery and the right; the left, colder heel had a weaker signal amplitude. These results suggest that thermal video subjected to EVM can reveal the pedal pulse rate by extracting intensity signals from areas in which arteries are close to the skin, and may reveal differences in the microvascular health of the left versus right foot. The ability to detect pedal pulse and differences in microvascular health using an inexpensive and non-intrusive thermal camera would of great value to a podiatric clinic.

Application of DCT-derived parameters for early detection of polyneuropathy in diabetic patients

Diabetic foot is one of the most severe complications of diabetes. Early diagnosis of this syndrome can ensure proper medical care and adequate treatment. Various image analysis methods can be used to speed up the diagnosis process, and automated diagnosis can be applied as a screening technique to reduce its cost. Introducing auxiliary diagnostic parameters may help to detect polyneuropathy or neuropathy, both of which often precede the appearance of diabetic foot syndrome. The present paper describes a study performed on a group of diabetic patients by analyzing plantar pressure distribution images. As part of this study, 2D discrete cosine transform (DCT) is computed for the forefoot and rearfoot regions of each diabetic subject in a group of 37 patients. Three new DCT-based parameters are introduced to help to detect polyneuropathy or at least indicate that the patient may have polyneuropathy without a time-consuming examination. The results indicate a certain relationship between these parameters and the presence of polyneuropathy. This information could be used in further diagnosis to prevent foot ulcers from developing in patients with diabetes.