In this paper, we have investigated the differences in the voices of Parkinson’s disease (PD) and age-matched control (CO) subjects when uttering three phonemes using two complexity measures: fractal dimension (FD) and normalised mutual information (NMI). Three sustained phonetic voice recordings, /a/, /u/ and /m/, from 22 CO (mean age = 66.91) and 24 PD (mean age = 71.83) participants were analysed. FD was first computed for PD and CO voice recordings, followed by the computation of NMI between the test groups: PD–CO, PD–PD and CO–CO. Four features reported in the literature—normalised pitch period entropy (Norm. PPE), glottal-to-noise excitation ratio (GNE), detrended fluctuation analysis (DFA) and glottal closing quotient (ClQ)—were also computed for comparison with the proposed complexity measures. The statistical significance of the features was tested using a one-way ANOVA test. Support vector machine (SVM) with a linear kernel was used to classify the test groups, using a leave-one-out validation method. The results showed that PD voice recordings had lower FD compared to CO (p < 0.008). It was also observed that the average NMI between CO voice recordings was significantly lower compared with the CO–PD and PD–PD groups (p < 0.036) for the three phonetic sounds. The average NMI and FD demonstrated higher accuracy (>80%) in differentiating the test groups compared with other speech feature-based classifications. This study has demonstrated that the voices of PD patients has reduced FD, and NMI between voice recordings of PD–CO and PD–PD is higher compared with CO–CO. This suggests that the use of NMI obtained from the sample voice, when paired with known groups of CO and PD, can be used to identify PD voices. These findings could have applications for population screening.
Diabetes is the most common cause of non-traumatic amputations worldwide, and education is key to prevention. Mobile phones and applications (apps) are increasingly being used. This study co-designed and assessed whether a foot health education app would be feasible and acceptable to support people with diabetes (PWD) to prevent serious foot complications.A diabetes foot app was co-designed with PWD, experts, researchers and biomedical engineers following co-design principles. The app was piloted in a convenience sample of adults with diabetes from one community health service in metropolitan Melbourne for 12 weeks. Baseline quantitative data were collected on foot health, knowledge, self-care behaviours and attitudes. Qualitative data were collected post intervention to capture experiences of using the app, using interviews and focus groups.The co-designed app included information on amputation risk and self-care practices to prevent serious foot complications. The content used images and simple wording, focusing on early help-seeking behaviour. Forty participants with a mean age of 66.9±17.1 years were included in the pilot. Seven participants withdrew due to personal and health-related issues.Uptake of the app was low, with 18 participants using the app for any period of time. Qualitative interviews or focus groups were undertaken with 31 participants. Overall, the information was perceived as highly useful for newly-diagnosed PWD and worth pursuing. Future work is needed to identify which PWD would most benefit, and incorporate aspects relating to increased opportunity and motivation for behaviour change and a centralised data management system to provide updates.
This study investigated the difference in the gait of patients with Parkinson’s disease (PD), age-matched controls and young controls during three walking patterns. Experiments were conducted with 24 PD, 24 age-matched controls and 24 young controls, and four gait intervals were measured using inertial measurement units (IMU). Group differences between the mean and variance of the gait parameters (stride interval, stance interval, swing interval and double support interval) for the three groups were calculated and statistical significance was tested. The results showed that the variance in each of the four gait parameters of PD patients was significantly higher compared with the controls, irrespective of the three walking patterns. This study showed that the variance of any of the gait interval parameters obtained using IMU during any of the walking patterns could be used to differentiate between the gait of PD and control people.
Diabetic foot infections are a major cause of hospitalization, and delayed treatment can lead to numerous complications. The aim of this research was to investigate high-resolution spectroscopy of the wound center and periwound area for real-time estimation of multispectral signature of bacteria at the base of diabetic foot ulcers. We investigated the spectrum of the reflected visual light from diabetic foot ulcers and developed a method that identifies the presence of bacteria in the wound infections. We undertook a prospective pilot study on 18 patients with type 1 and type 2 diabetes and chronic diabetic foot ulcers. The spectral coefficients were directly compared with the results from the wound swab. The results of the multispectral analysis demonstrated 100% sensitivity, with 100% negative predictive values of identifying the presence of the bacteria, which was the cause of the infection in the wound. The results of our study suggest that the changes in the multispectral properties of the wound can be used to identify the presence of bacteria in the infected area using a noninvasive device without any contact with the wound. This technique holds great promise for real-time objective evaluation of the wound infection status beyond the standard visual assessment of diabetic foot ulcers.
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