Background Parkinson’s disease is the second most common long-term chronic, progressive, neurodegenerative disease, affecting more than 10 million people worldwide. There has been a rising interest in wearable devices for evaluation of movement disorder diseases such as Parkinson’s disease due to the limitations in current clinic assessment methods such as Unified Parkinson’s Disease Rating Scale (UPDRS) and the Hoehn and Yahr (HY) scale. However, there are only a few commercial wearable devices available, which, in addition, have had very limited adoption and implementation. This inconsistency may be due to a lack of users’ perspectives in terms of device design and implementation. This study aims to identify the perspectives of healthcare professionals and patients linked to current assessment methods and to identify preferences, and requirements of wearable devices. Methods This was a qualitative study using semi-structured interviews followed by focus groups. Transcripts from sessions were analysed using an inductive thematic approach. Results It was noted that the well-known assessment process such as Unified Parkinson’s Disease Rating Scale (UPDRS) was not used routinely in clinics since it is time consuming, subjective, inaccurate, infrequent and dependent on patients’ memories. Participants suggested that objective assessment methods are needed to increase the chance of effective treatment. The participants’ perspectives were positive toward using wearable devices, particularly if they were involved in early design stages. Patients emphasized that the devices should be comfortable, but they did not have any concerns regarding device visibility or data privacy transmitted over the internet when it comes to their health. In terms of wearing a monitor, the preferable part of the body for all participants was the wrist. Healthcare professionals stated a need for an economical solution that is easy to interpret. Some design aspects identified by patients included clasps, material choice, and form factor. Conclusion The study concluded that current assessment methods are limited. Patients’ and healthcare professionals’ involvement in wearable devices design process has a pivotal role in terms of ultimate user acceptance. This includes the provision of additional functions to the wearable device, such as fall detection and medication reminders, which could be attractive features for patients.
Tremor is an indicative symptom of Parkinson’s disease (PD). Healthcare professionals have clinically evaluated the tremor as part of the Unified Parkinson’s disease rating scale (UPDRS) which is inaccurate, subjective and unreliable. In this study, a novel approach to enhance the tremor severity classification is proposed. The proposed approach is a combination of signal processing and resampling techniques; over-sampling, under-sampling and a hybrid combination. Resampling techniques are integrated with well-known classifiers, such as artificial neural network based on multi-layer perceptron (ANN-MLP) and random forest (RF). Advanced metrics are calculated to evaluate the proposed approaches such as area under the curve (AUC), geometric mean (Gmean) and index of balanced accuracy (IBA). The results show that over-sampling techniques performed better than other resampling techniques, also hybrid techniques performed better than under-sampling techniques. The proposed approach improved tremor severity classification significantly and show that the best approach to classify tremor severity is the combination of ANN-MLP with Borderline SMOTE which has obtained 93.81% overall accuracy, 96% Gmean, 91% IBA and 99% AUC. Besides, it is found that different resampling techniques performed differently with different classifiers.
Tremor is a common symptom of Parkinson’s disease (PD). Currently, tremor is evaluated clinically based on MDS-UPDRS Rating Scale, which is inaccurate, subjective, and unreliable. Precise assessment of tremor severity is the key to effective treatment to alleviate the symptom. Therefore, several objective methods have been proposed for measuring and quantifying PD tremor from data collected while patients performing scripted and unscripted tasks. However, up to now, the literature appears to focus on suggesting tremor severity classification methods without discrimination tasks effect on classification and tremor severity measurement. In this study, a novel approach to identify a recommended system is used to measure tremor severity, including the influence of tasks performed during data collection on classification performance. The recommended system comprises recommended tasks, classifier, classifier hyperparameters, and resampling technique. The proposed approach is based on the above-average rule of five advanced metrics results of four subdatasets, six resampling techniques, six classifiers besides signal processing, and features extraction techniques. The results of this study indicate that tasks that do not involve direct wrist movements are better than tasks that involve direct wrist movements for tremor severity measurements. Furthermore, resampling techniques improve classification performance significantly. The findings of this study suggest that a recommended system consists of support vector machine (SVM) classifier combined with BorderlineSMOTE oversampling technique and data collection while performing set of recommended tasks, which are sitting, stairs up and down, walking straight, walking while counting, and standing.
Background Parkinson's disease is the second most common long-term chronic, progressive, neurodegenerative disease, affecting more than 10 million people worldwide. There has been a rising interest in wearable devices for evaluation of movement disorder diseases such as Parkinson's disease due to the limitations in current clinic assessment methods such as Unified Parkinson's Disease Rating Scale. However, there are only a few commercial wearable devices available, which, in addition, have had very limited adoption and implementation. This inconsistency may be due to a lack of users’ perspectives in terms of device design and implementation.Objectives This study aims to identify the perspectives of healthcare professionals and patients linked to current assessment methods and to identify preferences, needs, and requirements of wearable devices. Methods This was a qualitative study using semi-structured interviews followed by focus groups. Transcripts from sessions were analysed using a thematic approach. Results It was noted that the well-known assessment and monitoring process such as Unified Parkinson's Disease Rating Scale was not used routinely in clinics since it is time consuming, subjective, inaccurate, infrequent and dependent on patients’ memories. Participants suggested that objective diagnosis and assessment methods are needed to increase the chance of effective treatment. The participants’ perspectives were positive toward using wearable devices, particularly if they were involved in early design stages. Patients emphasized that the devices should be comfortable, but they did not have any concerns regarding device visibility or data privacy transmitted over the internet when it comes to their health. In terms of wearing a monitor, the preferable part of the body for all participants was the wrist. Healthcare professionals stated a need for an economical solution that is easy to interpret. Some design aspects identified by patients included clasps, material choice, and form factor. Conclusion The study concluded that current diagnosis and assessment methods are limited. Patients’ and healthcare professionals’ involvement in wearable devices design process has a pivotal role in terms of ultimate user acceptance. This includes the provision of additional functions to the wearable device, such as fall detection and medication reminders, which could be attractive features for patients.
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