Parkinson’s Disease is a neurodegenerative disease that affects the aging population and is caused by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). With the onset of the disease, the patients suffer from mobility disorders such as tremors, bradykinesia, impairment of posture and balance, etc., and it progressively worsens in the due course of time. Additionally, as there is an exponential growth of the aging population in the world the number of people suffering from Parkinson’s Disease is increasing and it levies a huge economic burden on governments. However, until now no therapeutic method has been discovered for completely eradicating the disease from a person’s body after it’s onset. Therefore, the early detection of Parkinson’s Disease is of paramount importance to tackle the progressive loss of dopaminergic neurons in patients to serve them with a better life. In this study, 3T T1-weighted MRI scans were acquired from the Parkinson’s Progression Markers Initiative (PPMI) database of 406 subjects from baseline visit, where 203 were healthy and 203 were suffering from Parkinson’s Disease. Following data pre-processing, a 3D convolutional neural network (CNN) architecture was developed for learning the intricate patterns in the Magnetic Resonance Imaging (MRI) scans for the detection of Parkinson’s Disease. In the end, it was observed that the developed 3D CNN model performed superiorly by completely aligning with the hypothesis of the study and plotted an overall accuracy of 95.29%, average recall of 0.943, average precision of 0.927, average specificity of 0.9430, f1-score of 0.936, and Receiver Operating Characteristic—Area Under Curve (ROC-AUC) score of 0.98 for both the classes respectively.

The safety and welfare of companion animals such as dogs has become a large challenge in the last few years. To assess the well-being of a dog, it is very important for human beings to understand the activity pattern of the dog, and its emotional behavior. A wearable, sensor-based system is suitable for such ends, as it will be able to monitor the dogs in real-time. However, the question remains unanswered as to what kind of data should be used to detect the activity patterns and emotional patterns, as does another: what should be the location of the sensors for the collection of data and how should we automate the system? Yet these questions remain unanswered, because to date, there is no such system that can address the above-mentioned concerns. The main purpose of this study was (1) to develop a system that can detect the activities and emotions based on the accelerometer and gyroscope signals and (2) to automate the system with robust machine learning techniques for implementing it for real-time situations. Therefore, we propose a system which is based on the data collected from 10 dogs, including nine breeds of various sizes and ages, and both genders. We used machine learning classification techniques for automating the detection and evaluation process. The ground truth fetched for the evaluation process was carried out by taking video recording data in frame per second and the wearable sensors data were collected in parallel with the video recordings. Evaluation of the system was performed using an ANN (artificial neural network), random forest, SVM (support vector machine), KNN (k nearest neighbors), and a naïve Bayes classifier. The robustness of our system was evaluated by taking independent training and validation sets. We achieved an accuracy of 96.58% while detecting the activity and 92.87% while detecting emotional behavior, respectively. This system will help the owners of dogs to track their behavior and emotions in real-life situations for various breeds in different scenarios.

Fluctuations in motor symptoms are mostly observed in Parkinson’s disease (PD) patients. This characteristic is inevitable, and can affect the quality of life of the patients. However, it is difficult to collect precise data on the fluctuation characteristics using self-reported data from PD patients. Therefore, it is necessary to develop a suitable technology that can detect the medication state, also termed the “On”/“Off” state, automatically using wearable devices; at the same time, this could be used in the home environment. Recently, wearable devices, in combination with powerful machine learning techniques, have shown the potential to be effectively used in critical healthcare applications. In this study, an algorithm is proposed that can detect the medication state automatically using wearable gait signals. A combination of features that include statistical features and spatiotemporal gait features are used as inputs to four different classifiers such as random forest, support vector machine, K nearest neighbour, and Naïve Bayes. In total, 20 PD subjects with definite motor fluctuations have been evaluated by comparing the performance of the proposed algorithm in association with the four aforementioned classifiers. It was found that random forest outperformed the other classifiers with an accuracy of 96.72%, a recall of 97.35%, and a precision of 96.92%.

Detection of the state of mind has increasingly grown into a much favored study in recent years. After the advent of smart wearables in the market, each individual now expects to be delivered with state-of-the-art reports about his body. The most dominant wearables in the market often focus on general metrics such as the number of steps, distance walked, heart rate, oximetry, sleep quality, and sleep stage. But, for accurately identifying the well-being of an individual, another important metric needs to be analyzed, which is the state of mind. The state of mind is a metric of an individual that boils down to the activity of all other related metrics. But, the detection of the state of mind has formed a huge challenge for the researchers as a single biosignal cannot propose a particular decision threshold for detection. Therefore, in this work, multiple biosignals from different parts of the body are used to determine the state of mind of an individual. The biosignals, blood volume pulse (BVP), and accelerometer are intercepted from a wrist-worn wearable, and electrocardiography (ECG), electromyography (EMG), and respiration are intercepted from a chest-worn pod. For the classification of the biosignals to the multiple state-of-mind categories, a multichannel convolutional neural network architecture was developed. The overall model performed pretty well and pursued some encouraging results by demonstrating an average recall and precision of 97.238% and 97.652% across all the classes, respectively.

In the last few years, the importance of measuring gait characteristics has increased tenfold due to their direct relationship with various neurological diseases. As patients suffering from Parkinson’s disease (PD) are more prone to a movement disorder, the quantification of gait characteristics helps in personalizing the treatment. The wearable sensors make the measurement process more convenient as well as feasible in a practical environment. However, the question remains to be answered about the validation of the wearable sensor-based measurement system in a real-world scenario. This paper proposes a study that includes an algorithmic approach based on collected data from the wearable accelerometers for the estimation of the gait characteristics and its validation using the Tinetti mobility test and 3D motion capture system. It also proposes a machine learning-based approach to classify the PD patients from the healthy older group (HOG) based on the estimated gait characteristics. The results show a good correlation between the proposed approach, the Tinetti mobility test, and the 3D motion capture system. It was found that decision tree classifiers outperformed other classifiers with a classification accuracy of 88.46%. The obtained results showed enough evidence about the proposed approach that could be suitable for assessing PD in a home-based free-living real-time environment.

Parkinson’s disease is caused due to the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). Presently, with the exponential growth of the aging population across the world the number of people being affected by the disease is also increasing and it imposes a huge economic burden on the governments. However, to date, no therapy or treatment has been found that can completely eradicate the disease. Therefore, early detection of Parkinson’s disease is very important so that the progressive loss of dopaminergic neurons can be controlled to provide the patients with a better life. In this study, 3T T1-MRI scans were collected from 906 subjects, out of which, 203 are control subjects, 66 are prodromal subjects and 637 are Parkinson’s disease patients. To analyze the MRI scans for the detection of neurodegeneration and Parkinson’s disease, eight subcortical structures were segmented from the acquired MRI scans using atlas based segmentation. Further, on the extracted eight subcortical structures, feature extraction was performed to extract textural, morphological and statistical features, respectively. After the feature extraction process, an exhaustive set of 107 features were generated for each MRI scan. Therefore, a two-level feature extraction process was implemented for finding the best possible feature set for the detection of Parkinson’s disease. The two-level feature extraction procedure leveraged correlation analysis and recursive feature elimination, which at the end provided us with 20 best performing features out of the extracted 107 features. Further, all the features were trained using machine learning algorithms and a comparative analysis was performed between four different machine learning algorithms based on the selected performance metrics. And at the end, it was observed that artificial neural network (multi-layer perceptron) performed the best by providing an overall accuracy of 95.3%, overall recall of 95.41%, overall precision of 97.28% and f1-score of 94%, respectively.