Single Sensor Gait Analysis to Detect Diabetic Peripheral Neuropathy: A Proof of Principle Study

Abstract

This study explored the potential utility of gait analysis using a single sensor unit (inertial measurement unit [IMU]) as a simple tool to detect peripheral neuropathy in people with diabetes. Seventeen people (14 men) aged 63±9 years (mean±SD) with diabetic peripheral neuropathy performed a 10-m walk test instrumented with an IMU on the lower back. Compared to a reference healthy control data set (matched by gender, age, and body mass index) both spatiotemporal and gait control variables were different between groups, with walking speed, step time, and SDa (gait control parameter) demonstrating good discriminatory power (receiver operating characteristic area under the curve >0.8). These results provide a proof of principle of this relatively simple approach which, when applied in clinical practice, can detect a signal from those with known diabetes peripheral neuropathy. The technology has the potential to be used both routinely in the clinic and for tele-health applications. Further research should focus on investigating its efficacy as an early indicator of or effectiveness of the management of peripheral neuropathy. This could support the development of interventions to prevent complications such as foot ulceration or Charcot's foot.

Association between gait and cognition in an elderly population based sample

“…Relative variability of spatial parameters is defined as the coefficient of variation (CoV), defined as the ratio of the standard deviation to the average of stride time and length. Validation studies have been conducted on this approach in health [ 23 , 24 ] and clinical conditions [ [25] , [26] , [27] ] with and without gait abnormalities. An average of 13 steps, equalling 6 strides per 10 m-walk were recorded.…”

Wearable Health Technology to Quantify the Functional Impact of Peripheral Neuropathy on Mobility in Parkinson’s Disease: A Systematic Review

“…Another study by Kang [ 42 ] showed improvement in stride velocity, stride length, and double limb support (%) during dual-task and fast walking, compared to single-task, after plantar mechanical stimulation. Differences from controls were found in step time, cadence, and gait speed but not in stride length in a study by Esser et al [ 17 ], and gait speed was also 10% decreased in DPN group compared to controls in a study by Ling et al [ 31 ]. Another important result was pointed out by Najafi et al [ 34 ], who found differences in spatiotemporal parameters only during long distances, especially in gait variability and in double support time, when comparing DPN patients with controls.…”

“…Mobility assessment with wearable health technologies are widely investigated in a variety of illnesses, particularly in PD, and allows high sensitivity, accuracy, and reproducibility [ 16 ]. However, these methodologies are scarcely studied and have yet to be explored in PNP [ 17 ], although a small number of previous works using wearable sensors have successfully demonstrated motor and physical activity characteristics in PNP compared to controls [ 18 , 19 ]. Since the presence of PNP has only recently been considered related to PD, we were interested in understanding whether PNP-PD patients showed specific motor deficits, which can be measured with the use of wearable health technology.…”

Functional Balance and Gait Characteristics in Men With Lower Urinary Tract Symptoms Secondary to Benign Prostatic Hyperplasia

“…Measures of functional balance and gait have been increasingly used to distinguish and monitor individuals with various disorders (Cimolin et al, 2011; Esser et al, 2018; Juen et al, 2014; Kuate-Tegueu et al, 2017; Michael et al, 2005; Valkanova et al, 2018; Zago et al, 2018). Gait and balance deficits are particularly compromised during dual-task requiring additional attention and may lead to decreased mobility and safety in community environments (Al-Yahya et al, 2011; Beauchet et al, 2008, 2009; Shumway-Cook & Woolacott, 2000).…”

“…In line with this premise, gait and walking speed reflect health, well-being, and morbidity (Juen, Cheng, Prieto-Centurion, Krishnan, & Schatz, 2014; Studenski et al, 2011), with gait characteristics having been reported to predict fall (van Schooten et al, 2015; Weiss et al, 2013) and differentiate fallers from non-fallers (Howcroft, Kofman, & Lemaire, 2013; Rispens et al, 2015; Toebes, Hoozemans, Furrer, Dekker, & van Dieen, 2012). For example, gait has been increasingly used to monitor individuals with various disorders affecting neurological, cardiovascular, metabolic, and musculoskeletal systems (Cimolin et al, 2011; Esser et al, 2018; Juen et al, 2014; Kuate-Tegueu et al, 2017; Michael, Allen, & Macko, 2005; Valkanova et al, 2018; Zago et al, 2018). In community-dwelling men, LUTS have been suggested to be a potential risk factor for falls (Soliman, Meyer, & Baum, 2016).…”