Towards the Development of a Wearable Tremor Suppression Glove

Patients diagnosed with Parkinson’s disease (PD) often associate with tremor. Among other symptoms of PD, tremor is the most aggressive symptom and it is difficult to control with traditional treatments. This thesis presents the assessment of Parkinsonian hand tremor in both the time domain and the frequency domain, the performance of a tremor estimator using different tremor models, and the development of a novel mechatronic transmission system for a wearable tremor suppression device. This transmission system functions as a mechatronic splitter that allows a single power source to support multiple independent applications. Unique features of this transmission system include low power consumption and adjustability in size and weight. Tremor assessment results showed that the hand tremor signal often presents a multi-harmonics pattern. The use of a multi-harmonics tremor model produced a better estimation result than using a monoharmonic tremor model

Linear modeling of possible mechanisms for parkinson tremor generation

The power of Parkinson tremor is expressed in terms of possibly changed frequency response functions between relevant variables in the neuromuscular system. The derivation starts out from a linear loopless equivalent model of mechanisms for general tremor generation. Hypothetical changes in this model from the substrate of the disease are indicated, and possible ones are inferred from literature about experiments on patients. The result indicates that in these patients tremor appears to have been generated in loops, which did not include the brain area which in surgery usually is inactivated. For some patients in the literature, these loops could involve muscle length receptors, the static sensitivity of which may have been enlarged by pathological brain activity

A Wearable Mechatronic Device for Hand Tremor Monitoring and Suppression: Development and Evaluation

Tremor, one of the most disabling symptoms of Parkinson\u27s disease (PD), significantly affects the quality of life of the individuals who suffer from it. These people live with difficulties with fine motor tasks, such as eating and writing, and suffer from social embarrassment. Traditional medicines are often ineffective, and surgery is highly invasive and risky. The emergence of wearable technology facilitates an externally worn mechatronic tremor suppression device as a potential alternative approach for tremor management. However, no device has been developed for the suppression of finger tremor that has been validated on a human. It has been reported in the literature that tremor can be selectively suppressed by mechanical loading. Therefore, the objectives of this thesis were to develop a wearable tremor suppression device that can suppress tremor at the wrist and the fingers, and to evaluate it on individuals with PD in a pre-clinical trial. To address these objectives, several experiments were performed to quantify hand tremor; an enhanced high-order tremor estimator was developed and evaluated for tremor estimation; and a wearable tremor suppression glove (WTSG) was developed to suppress tremor in the index finger metacarpophalangeal (MCP) joint, the thumb MCP joint, and the wrist. A total of 18 individuals with PD were recruited for characterizing tremor. The frequencies and magnitudes of the linear acceleration, angular velocity, and angular displacement of tremor in the index finger MCP joint, the thumb MCP joint, and the wrist were quantified. The results showed that parkinsonian tremor consists of multiple harmonics, and that the second and third harmonics cannot be ignored. With the knowledge of the tremor characteristics, an enhanced high-order tremor estimator was developed to acquire better tremor estimation accuracy than its lower-order counterpart. In addition, the evaluation of the WTSG was conducted on both a physical tremor simulator and on one individual with PD. The results of the simulation study proved the feasibility of using the WTSG to suppress tremor; and the results of the evaluation on a human subject showed that the WTSG can suppress tremor motion while allowing the user to perform voluntary motions. The WTSG developed as a result of this work has demonstrated the feasibility of managing hand tremor with a mechatronic device, and its validation on a human subject has provided useful insights from the user\u27s perspectives, which facilitate the transition of the WTSG from the lab to the clinic, and eventually to commercial use. Lastly, an evaluation studying the impact of suppressed tremor on unrestricted joints was conducted on 14 individuals with PD. The results showed a significant increase in tremor magnitude in the unrestricted distal joints when the motions of the proximal joints were restricted. The average increase of the tremor magnitude of the index finger MCP joint, the thumb MCP joint, the wrist and the elbow are 54%, 96%, 124%, and 98% for resting tremor, and 50%, 102%, 49%, and 107% for postural tremor, respectively. Such a result provided additional clinical justification for the significance of the development of a wearable mechatronic device for hand tremor management. Although the focus of this thesis is on hand tremor management, the development and evaluation of a full upper-limb tremor suppression device is required as a future step, in order to advance the use of wearable mechatronic devices as one of the valid tremor treatment approaches

Angular velocity analysis boosted by machine learning for helping in the differential diagnosis of Parkinson’s Disease and Essential Tremor

Recent research has shown that smartphones/smartwatches have a high potential to help physicians to identify and differentiate between different movement disorders. This work aims to develop Machine Learning models to improve the differential diagnosis between patients with Parkinson’s Disease and Essential Tremor. For this purpose, we use a mobile phone’s built-in gyroscope to record the angular velocity signals of two different arm positions during the patient’s follow-up, more precisely, in rest and posture positions. To develop and to find the best classification models, diverse factors were considered, such as the frequency range, the training and testing divisions, the kinematic features, and the classification method. We performed a two-stage kinematic analysis, first to differentiate between healthy and trembling subjects and then between patients with Parkinson’s Disease and Essential Tremor. The models developed reached an average accuracy of 97.2+/-3.7% (98.5% Sensitivity, 93.3% Specificity) to differentiate between Healthy and Trembling subjects and an average accuracy of 77.8+/-9.9% (75.7% Sensitivity, 80.0% Specificity) to discriminate between Parkinson’s Disease and Essential Tremor patients. Therefore, we conclude, that the angular velocity signal can be used to develop Machine Learning models for the differential diagnosis of Parkinson’s disease and Essential Tremor.Peer ReviewedPostprint (published version

A Movement-Tremors Recorder for Patients of Neurodegenerative Diseases

Neurodegenerative diseases such as Alzheimer, Parkinson, motor neuron, and Chorea affect millions of people today. Their effect on the central nervous system causes the loss of brain functions as well as motor disturbances and sometimes cognitive deficits. In such a scenario, the monitoring and evaluation of early symptoms are mandatory for the improvement of the patient's quality of life. Here, the authors describe the development, the laboratory calibration, and the "in-field validation" under the medical supervision of a movement tremors recorder for subjects affected by neurodegenerative diseases. The developed device is based on an array of four accelerometers connected to an embedded development board. This system is able to monitor tremor/movement, accidental falls, and, moreover, it can track the Alzheimer subjects' geographical position. A remote supervisor can collect data from the system through Bluetooth, Wi-Fi, or GSM connections. A data compression algorithm was developed directly on board in order to increase the efficiency of data transmission and reduce power consumptions

Differential diagnosis between Parkinson's disease and essential tremor using the smartphone's accelerometer

Background: The differential diagnosis between patients with essential tremor (ET) and those with Parkinson's disease (PD) whose main manifestation is tremor may be difficult unless using complex neuroimaging techniques such as 123I-FP-CIT SPECT. We considered that using smartphone's accelerometer to stablish a diagnostic test based on time-frequency differences between PD an ET could support the clinical diagnosis. Methods: The study was carried out in 17 patients with PD, 16 patients with ET, 12 healthy volunteers and 7 patients with tremor of undecided diagnosis (TUD), who were re-evaluated one year after the first visit to reach the definite diagnosis. The smartphone was placed over the hand dorsum to record epochs of 30 s at rest and 30 s during arm stretching. We generated frequency power spectra and calculated receiver operating characteristics curves (ROC) curves of total spectral power, to establish a threshold to separate subjects with and without tremor. In patients with PD and ET, we found that the ROC curve of relative energy was the feature discriminating better between the two groups. This threshold was then used to classify the TUD patients. Results: We could correctly classify 49 out of 52 subjects in the category with/without tremor (97.96% sensitivity and 83.3% specificity) and 27 out of 32 patients in the category PD/ET (84.38% discrimination accuracy). Among TUD patients, 2 of 2 PD and 2 of 4 ET were correctly classified, and one patient having PD plus ET was classified as PD. Conclusions: Based on the analysis of smartphone accelerometer recordings, we found several kinematic features in the analysis of tremor that distinguished first between healthy subjects and patients and, ultimately, between PD and ET patients. The proposed method can give immediate results for the clinician to gain valuable information for the diagnosis of tremor. This can be useful in environments where more sophisticated diagnostic techniques are unavailable

Upper limb motor pre-clinical assessment in Parkinson's disease using machine learning

Abstract Introduction Parkinson's disease (PD) is a common neurodegenerative disorder characterized by disabling motor and non-motor symptoms. For example, idiopathic hyposmia (IH), which is a reduced olfactory sensitivity, is typical in >95% of PD patients and is a preclinical marker for the pathology. Methods In this work, a wearable inertial device, named SensHand V1, was used to acquire motion data from the upper limbs during the performance of six tasks selected by MDS-UPDRS III. Three groups of people were enrolled, including 30 healthy subjects, 30 IH people, and 30 PD patients. Forty-eight parameters per side were computed by spatiotemporal and frequency data analysis. A feature array was selected as the most significant to discriminate among the different classes both in two-group and three-group classification. Multiple analyses were performed comparing three supervised learning algorithms, Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes, on three different datasets. Results Excellent results were obtained for healthy vs. patients classification (F-Measure 0.95 for RF and 0.97 for SVM), and good results were achieved by including subjects with hyposmia as a separate group (0.79 accuracy, 0.80 precision with RF) within a three-group classification. Overall, RF classifiers were the best approach for this application. Conclusion The system is suitable to support an objective PD diagnosis. Further, combining motion analysis with a validated olfactory screening test, a two-step non-invasive, low-cost procedure can be defined to appropriately analyze people at risk for PD development, helping clinicians to identify also subtle changes in motor performance that characterize PD onset