The aim of this study is to propose a practical smartphone-based tool to accurately assess upper limb tremor in Parkinson's disease (PD) patients. The tool uses signals from the phone's accelerometer and gyroscope (as the phone is held or mounted on a subject's hand) to compute a set of metrics which can be used to quantify a patient's tremor symptoms. In a small-scale clinical study with 25 PD patients and 20 age-matched healthy volunteers, we combined our metrics with machine learning techniques to correctly classify 82% of the patients and 90% of the healthy volunteers, which is high compared to similar studies. The proposed method could be effective in assisting physicians in the clinic, or to remotely evaluate the patient's condition and communicate the results to the physician. Our tool is low cost, platform independent, noninvasive, and requires no expertise to use. It is also well matched to the standard clinical examination for PD and can keep the patient "connected" to his physician on a daily basis. Finally, it can facilitate the creation of anonymous profiles for PD patients, aiding further research on the effectiveness of medication or other overlooked aspects of patients' lives.

No abstract

With an ever-growing number of technologically advanced methods for the diagnosis and quantification of movement disorders, comes the need to assess their accuracy and see how they match up with widely used standard clinical assessment tools. This work compares quantitative measurements of hand tremor in twenty-three Parkinson's disease patients, with their clinical scores in the hand tremor components of the Unified Parkinson's Disease Rating Scale (UPDRS), which is considered the "gold standard" in the clinical assessment of the disease. Our measurements were obtained using a smartphone-based platform, which processes the phone's accelerometer and gyroscope signals to detect and measure hand tremor. The signal metrics used were mainly based on the magnitude of the acceleration and the rotation rate vectors of the device. Our results suggest relatively strong correlation (r>0.7 and p<;0.01) between the patients' UPDRS hand tremor scores and the signal metrics applied to the measured signals.

Recent advances in mobile phone technology have placed an impressive array of sensing and communication equipment at the hands of an ever-growing number of people. One of the areas which can potentially be transformed by the availability of what is essentially a cheap, ubiquitous networked sensor, is that of remote diagnosis of movement disorders, such as Parkinson's disease. This work describes a smartphone-based method for detecting and quantifying the hand tremor associated with movement disorders using signals from the accelerometer and gyroscope embedded in the patient's phone. Our approach is web-based and user-friendly, requiring minimal user interaction. In clinical experiments with twenty subjects, we found that by combining both accelerometer and gyroscope signals, we were able to correctly identify those with hand tremor, using very simple signal metrics.

Background: Accurate assessment of symptoms in Parkinson's disease (PD) is essential for optimal treatment decisions. During the past few years, different monitoring modalities have started to be used in the everyday clinical practice, mainly for the evaluation of motor symptoms. However, monitoring technologies for PD have not yet gained wide acceptance among physicians, patients, and caregivers. The COVID-19 pandemic disrupted the patients' access to healthcare, bringing to the forefront the need for wearable sensors, which provide effective remote symptoms' evaluation and follow-up. Case Report: We report two cases with PD, whose symptoms were monitored with a new wearable CEmarked system (PDMonitor ® ), enabling appropriate treatment modifications. Conclusion: Objective assessment of the patient's motor symptoms in his daily home environment is essential for an accurate monitoring in PD and enhances treatment decisions.