Using a smart phone as a standalone platform for detection and monitoring of pathological tremors

The Role of Wearable Devices in Chronic Disease Monitoring and Patient Care: A Comprehensive Review

Wearable health devices are becoming vital in chronic disease management because they offer real-time monitoring and personalized care. This review explores their effectiveness and challenges across medical fields, including cardiology, respiratory health, neurology, endocrinology, orthopedics, oncology, and mental health. A thorough literature search identified studies focusing on wearable devices' impact on patient outcomes. In cardiology, wearables have proven effective for monitoring hypertension, detecting arrhythmias, and aiding cardiac rehabilitation. In respiratory health, these devices enhance asthma management and continuous monitoring of critical parameters. Neurological applications include seizure detection and Parkinson's disease management, with wearables showing promising results in improving patient outcomes. In endocrinology, wearable technology advances thyroid dysfunction monitoring, fertility tracking, and diabetes management. Orthopedic applications include improved postsurgical recovery and rehabilitation, while wearables help in early complication detection in oncology. Mental health benefits include anxiety detection, post-traumatic stress disorder management, and stress reduction through wearable biofeedback. In conclusion, wearable health devices offer transformative potential for managing chronic illnesses by enhancing real-time monitoring and patient engagement. Despite significant improvements in adherence and outcomes, challenges with data accuracy and privacy persist. However, with ongoing innovation and collaboration, we can all be part of the solution to maximize the benefits of wearable technologies in healthcare.

Upper limb intention tremor assessment: opportunities and challenges in wearable technology

BACKGROUND

Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature.

METHODOLOGY & RESULTS

This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table.

CONCLUSIONS

Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.

A standardized accelerometry method for characterizing tremor: Application and validation in an ageing population with postural and action tremor

Background

Ordinal scales based on qualitative observation are the mainstay in the clinical assessment of tremor, but are limited by inter-rater reliability, measurement precision, range, and ceiling effects. Quantitative tremor evaluation is well-developed in research, but clinical application has lagged, in part due to cumbersome mathematical application and lack of established standards.

Objectives

To develop a novel method for evaluating tremor that integrates a standardized clinical exam, wrist-watch accelerometers, and a software framework for data analysis that does not require advanced mathematical or computing skills. The utility of the method was tested in a sequential cohort of patients with predominant postural and action tremor presenting to a specialized surgical clinic with the presumptive diagnosis of Essential Tremor (ET).

Methods

Wristwatch accelerometry was integrated with a standardized clinical exam. A MATLAB application was developed for automated data analysis and graphical representation of tremor. Measures from the power spectrum of acceleration of tremor in different upper limb postures were derived in 25 consecutive patients. The linear results from accelerometry were correlated with the commonly used non-linear Clinical Rating Scale for Tremor (CRST).

Results

The acceleration power spectrum was reliably produced in all consecutive patients. Tremor frequency was stable in different postures and across patients. Both total and peak power of acceleration during postural conditions correlated well with the CRST. The standardized clinical examination with integrated accelerometry measures was therefore effective at characterizing tremor in a population with predominant postural and action tremor. The protocol is also illustrated on repeated measures in an ET patient who underwent Magnetic Resonance-Guided Focused Ultrasound thalamotomy.

Conclusion

Quantitative assessment of tremor as a continuous variable using wristwatch accelerometry is readily applicable as a clinical tool when integrated with a standardized clinical exam and a user-friendly software framework for analysis. The method is validated for patients with predominant postural and action tremor, and can be adopted for characterizing tremor of different etiologies with dissemination in a wide variety of clinical and research contexts in ageing populations.

Are smartphones and machine learning enough to diagnose tremor?

BACKGROUND

Patients with essential tremor (ET), Parkinson's disease (PD) and dystonic tremor (DT) can be difficult to classify and often share similar characteristics.

OBJECTIVES

To use ubiquitous smartphone accelerometers with and without clinical features to automate tremor classification using supervised machine learning, and to use unsupervised learning to evaluate if natural clusterings of patients correspond to assigned clinical diagnoses.

METHODS

A supervised machine learning classifier was trained to classify 78 tremor patients using leave-one-out cross-validation to estimate performance on unseen accelerometer data. An independent cohort of 27 patients were also studied. Next, we focused on a subset of 48 patients with both smartphone-based tremor measurements and detailed clinical assessment metrics and compared two separate machine learning classifiers trained on these data.

RESULTS

The classifier yielded a total accuracy of 74.4% and F1-score of 0.74 for a trinary classification with an area under the curve of 0.904, average F1-score of 0.94, specificity of 97% and sensitivity of 84% in classifying PD from ET or DT. The algorithm classified ET from non-ET with 88% accuracy, but only classified DT from non-DT with 29% accuracy. A poorer performance was found in the independent cohort. Classifiers trained on accelerometer and clinical data respectively obtained similar results.

CONCLUSIONS

Machine learning classifiers achieved a high accuracy of PD, however moderate accuracy of ET, and poor accuracy of DT classification. This underscores the difficulty of using AI to classify some tremors due to lack of specificity in clinical and neuropathological features, reinforcing that they may represent overlapping syndromes.

Wearable sensors during drawing tasks to measure the severity of essential tremor

Commonly used methods to assess the severity of essential tremor (ET) are based on clinical observation and lack objectivity. This study proposes the use of wearable accelerometer sensors for the quantitative assessment of ET. Acceleration data was recorded by inertial measurement unit (IMU) sensors during sketching of Archimedes spirals in 17 ET participants and 18 healthy controls. IMUs were placed at three points (dorsum of hand, posterior forearm, posterior upper arm) of each participant's dominant arm. Movement disorder neurologists who were blinded to clinical information scored ET patients on the Fahn-Tolosa-Marin rating scale (FTM) and conducted phenotyping according to the recent Consensus Statement on the Classification of Tremors. The ratio of power spectral density of acceleration data in 4-12 Hz to 0.5-4 Hz bands and the total duration of the action were inputs to a support vector machine that was trained to classify the ET subtype. Regression analysis was performed to determine the relationship of acceleration and temporal data with the FTM scores. The results show that the sensor located on the forearm had the best classification and regression results, with accuracy of 85.71% for binary classification of ET versus control. There was a moderate to good correlation (r2 = 0.561) between FTM and a combination of power spectral density ratio and task time. However, the system could not accurately differentiate ET phenotypes according to the Consensus classification scheme. Potential applications of machine-based assessment of ET using wearable sensors include clinical trials and remote monitoring of patients.

Novel Device Used to Monitor Hand Tremors during Nocturnal Hypoglycemic Events

Diabetes is one of the lifelong diseases that require systematic medical care to avoid life-menacing ramifications. Uncontrolled diabetes can cause severe damage to most internal body organs, probably leading to death. Particularly, nocturnal hypoglycemic that occur usually at night during sleep. Severe cases of these events can lead to seizures, fainting, loss of consciousness, and death. The current medical devices lack to give the warning to reduce the risk of acquiring nocturnal hypoglycemic events because they use only for glucose monitoring during waking times. Consequently, the main goal of this work is to design and implement a new wearable device to detect and monitor tremors, which occur when a user has hypoglycemia (low blood sugar). The device can detect a frequency range of 4–12 Hz by using the accelerometer of Arduino Nano 33 BLE. It can send a signal to the phone application (app) via Bluetooth Low Energy (BLE). Once the phone receives a signal, the phone application can activate an alarm system to wake up the patient, call three selected contacts number, and universal emergency number. In case of the user is unresponsive, the app can provide the patient’s location, name, and date of birth to the emergency contacts numbers and universal emergency number. Additionally, the device cost is economically feasible and competitive compared to other medical devices.

Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor impairments such as tremor, bradykinesia, dyskinesia, and gait abnormalities. Current protocols assess PD symptoms during clinic visits and can be subjective. Patient diaries can help clinicians evaluate at-home symptoms, but can be incomplete or inaccurate. Therefore, researchers have developed in-home automated methods to monitor PD symptoms to enable data-driven PD diagnosis and management. We queried the US National Library of Medicine PubMed database to analyze the progression of the technologies and computational/machine learning methods used to monitor common motor PD symptoms. A sub-set of roughly 12,000 papers was reviewed that best characterized the machine learning and technology timelines that manifested from reviewing the literature. The technology used to monitor PD motor symptoms has advanced significantly in the past five decades. Early monitoring began with in-lab devices such as needle-based EMG, transitioned to in-lab accelerometers/gyroscopes, then to wearable accelerometers/gyroscopes, and finally to phone and mobile & web application-based in-home monitoring. Significant progress has also been made with respect to the use of machine learning algorithms to classify PD patients. Using data from different devices (e.g., video cameras, phone-based accelerometers), researchers have designed neural network and non-neural network-based machine learning algorithms to categorize PD patients across tremor, gait, bradykinesia, and dyskinesia. The five-decade co-evolution of technology and computational techniques used to monitor PD motor symptoms has driven significant progress that is enabling the shift from in-lab/clinic to in-home monitoring of PD symptoms.

Real-time classification of movement patterns of tremor patients

The process of diagnosing tremor patients often leads to misdiagnoses. Therefore, existing technical methods for analysing tremor are needed to more effectively distinguish between different diseases. For this purpose, a system has been developed that classifies measured tremor signals in real time. To achieve this, the hand tremor of 561 subjects has been measured in different hand positions. Acceleration and surface electromyography are recorded during the examination. For this study, data from subjects with Parkinson's Disease, Essential Tremor, and physiological tremor are considered. In a first signal analysis feature extraction is performed, and the resulting features are examined for their discriminative value. In a second step, three classification models based on different pattern recognition techniques are developed to classify the subjects with respect to their tremor type. With a trained decision tree, the three tremor types can be classified with a relative diagnostic accuracy of 83.14%. A neural network achieves 84.24% and the combination of both classifiers yields a relative diagnostic accuracy of 85.76%. The approach is promising and involving more features of the recorded time series will improve the discriminative value.

Comprehensive analysis of resting tremor based on acceleration signals of patients with Parkinson's disease

BACKGROUND

Resting tremor is an essential characteristic in patients suffering from Parkinson's disease (PD).

OBJECTIVE

Quantification and monitoring of tremor severity is clinically important to help achieve medication or rehabilitation guidance in daily monitoring.

METHODS

Wrist-worn tri-axial accelerometers were utilized to record the long-term acceleration signals of PD patients with different tremor severities rated by Unified Parkinson's Disease Rating Scale (UPDRS). Based on the extracted features, three kinds of classifiers were used to identify different tremor severities. Statistical tests were further designed for the feature analysis.

RESULTS

The support vector machine (SVM) achieved the best performance with an overall accuracy of 94.84%. Additional feature analysis indicated the validity of the proposed feature combination and revealed the importance of different features in differentiating tremor severities.

CONCLUSION

The present work obtains a high-accuracy classification in tremor severity, which is expected to play a crucial role in PD treatment and symptom monitoring in real life.

Could New Generations of Sensors Reshape the Management of Parkinson’s Disease?

Parkinson's disease (PD) is a chronic neurologic disease that has a great impact on the patient’s quality of life. The natural course of the disease is characterized by an insidious onset of symptoms, such as rest tremor, shuffling gait, bradykinesia, followed by improvement with the initiation of dopaminergic therapy. However, this “honeymoon period” gradually comes to an end with the emergence of motor fluctuations and dyskinesia. PD patients need long-term treatments and monitoring throughout the day; however, clinical examinations in hospitals are often not sufficient for optimal management of the disease. Technology-based devices are a new comprehensive assessment method of PD patient’s symptoms that are easy to use and give unbiased measurements. This review article provides an exhaustive overview of motor complications of advanced PD and new approaches to the management of the disease using sensors.

A-WEAR Bracelet for Detection of Hand Tremor and Bradykinesia in Parkinson's Patients

Parkinson's disease patients face numerous motor symptoms that eventually make their life different from those of normal healthy controls. Out of these motor symptoms, tremor and bradykinesia, are relatively prevalent in all stages of this disease. The assessment of these symptoms is usually performed by traditional methods where the accuracy of results is still an open question. This research proposed a solution for an objective assessment of tremor and bradykinesia in subjects with PD (10 older adults aged greater than 60 years with tremor and 10 older adults aged greater than 60 years with bradykinesia) and 20 healthy older adults aged greater than 60 years. Physical movements were recorded by means of an AWEAR bracelet developed using inertial sensors, i.e., 3D accelerometer and gyroscope. Participants performed upper extremities motor activities as adopted by neurologists during the clinical assessment based on Unified Parkinson's Disease Rating Scale (UPDRS). For discriminating the patients from healthy controls, temporal and spectral features were extracted, out of which non-linear temporal and spectral features show greater difference. Both supervised and unsupervised machine learning classifiers provide good results. Out of 40 individuals, neural net clustering discriminated 34 individuals in correct classes, while the KNN approach discriminated 91.7% accurately. In a clinical environment, the doctor can use the device to comprehend the tremor and bradykinesia of patients quickly and with higher accuracy.

Assessment of real life eating difficulties in Parkinson's disease patients by measuring plate to mouth movement elongation with inertial sensors

Parkinson’s disease (PD) is a neurodegenerative disorder with both motor and non-motor symptoms. Despite the progressive nature of PD, early diagnosis, tracking the disease’s natural history and measuring the drug response are factors that play a major role in determining the quality of life of the affected individual. Apart from the common motor symptoms, i.e., tremor at rest, rigidity and bradykinesia, studies suggest that PD is associated with disturbances in eating behavior and energy intake. Specifically, PD is associated with drug-induced impulsive eating disorders such as binge eating, appetite-related non-motor issues such as weight loss and/or gain as well as dysphagia—factors that correlate with difficulties in completing day-to-day eating-related tasks. In this work we introduce Plate-to-Mouth (PtM), an indicator that relates with the time spent for the hand operating the utensil to transfer a quantity of food from the plate into the mouth during the course of a meal. We propose a two-step approach towards the objective calculation of PtM. Initially, we use the 3D acceleration and orientation velocity signals from an off-the-shelf smartwatch to detect the bite moments and upwards wrist micromovements that occur during a meal session. Afterwards, we process the upwards hand micromovements that appear prior to every detected bite during the meal in order to estimate the bite’s PtM duration. Finally, we use a density-based scheme to estimate the PtM durations distribution and form the in-meal eating behavior profile of the subject. In the results section, we provide validation for every step of the process independently, as well as showcase our findings using a total of three datasets, one collected in a controlled clinical setting using standardized meals (with a total of 28 meal sessions from 7 Healthy Controls (HC) and 21 PD patients) and two collected in-the-wild under free living conditions (37 meals from 4 HC/10 PD patients and 629 meals from 3 HC/3 PD patients, respectively). Experimental results reveal an Area Under the Curve (AUC) of 0.748 for the clinical dataset and 0.775/1.000 for the in-the-wild datasets towards the classification of in-meal eating behavior profiles to the PD or HC group. This is the first work that attempts to use wearable Inertial Measurement Unit (IMU) sensor data, collected both in clinical and in-the-wild settings, towards the extraction of an objective eating behavior indicator for PD.

Patterns and Perceptions of Smartphone Use Among Academic Neurologists in the United States: Questionnaire Survey

Background

Smartphone technology is ubiquitous throughout neurologic practices, and numerous apps relevant to a neurologist’s clinical practice are now available. Data from other medical specialties suggest high utilization of smartphones in routine clinical care. However, the ways in which these devices are used by neurologists for patient care–related activities are not well defined.

Objective

This paper aims to characterize current patterns of smartphone use and perceptions of the utility of smartphones for patient care–related activities among academic neurology trainees and attending physicians. We also seek to characterize areas of need for future app development.

Methods

We developed a 31-item electronic questionnaire to address these questions and invited neurology trainees and attendings of all residency programs based in the United States to participate. We summarized descriptive statistics for respondents and specifically compared responses between trainees and attending physicians.

Results

We received 213 responses, including 112 trainee and 87 attending neurologist responses. Neurology trainees reported more frequent use of their smartphone for patient care–related activities than attending neurologists (several times per day: 84/112, 75.0% of trainees; 52/87, 59.8% of attendings; P=.03). The most frequently reported activities were internet use, calendar use, communication with other physicians, personal education, and health care–specific app use. Both groups also reported regular smartphone use for the physical examination, with trainees again reporting more frequent usage compared with attendings (more than once per week: 35/96, 36.5% of trainees; 8/58, 13.8% of attendings; P=.03). Respondents used their devices most commonly for the vision, cranial nerve, and language portions of the neurologic examination. The majority of respondents in both groups reported their smartphones as “very useful” or “essential” for the completion of patient care–related activities (81/108, 75.0% of trainees; 50/83, 60.2% of attendings; P=.12). Neurology trainees reported a greater likelihood of using their smartphones in the future than attending neurologists (“very likely”: 73/102, 71.6% of trainees; 40/82, 48.8% of attendings; P=.005). The groups differed in their frequencies of device usage for specific patient care–related activities, with trainees reporting higher usage for most activities. Despite high levels of use, only 12 of 184 (6.5%) respondents reported ever having had any training on how to use their device for clinical care. Regarding future app development, respondents rated vision, language, mental status, and cranial nerve testing as potentially being the most useful to aid in the performance of the neurologic examination.

Conclusions

Smartphones are used frequently and are subjectively perceived to be highly useful by academic neurologists. Trainees tended to use their devices more frequently than attendings. Our results suggest specific avenues for future technological development to improve smartphone use for patient care–related activities. They also suggest an unmet need for education on effectively using smartphone technology for clinical care.

Unobtrusive detection of Parkinson's disease from multi-modal and in-the-wild sensor data using deep learning techniques

Parkinson’s Disease (PD) is the second most common neurodegenerative disorder, affecting more than 1% of the population above 60 years old with both motor and non-motor symptoms of escalating severity as it progresses. Since it cannot be cured, treatment options focus on the improvement of PD symptoms. In fact, evidence suggests that early PD intervention has the potential to slow down symptom progression and improve the general quality of life in the long term. However, the initial motor symptoms are usually very subtle and, as a result, patients seek medical assistance only when their condition has substantially deteriorated; thus, missing the opportunity for an improved clinical outcome. This situation highlights the need for accessible tools that can screen for early motor PD symptoms and alert individuals to act accordingly. Here we show that PD and its motor symptoms can unobtrusively be detected from the combination of accelerometer and touchscreen typing data that are passively captured during natural user-smartphone interaction. To this end, we introduce a deep learning framework that analyses such data to simultaneously predict tremor, fine-motor impairment and PD. In a validation dataset from 22 clinically-assessed subjects (8 Healthy Controls (HC)/14 PD patients with a total data contribution of 18.305 accelerometer and 2.922 typing sessions), the proposed approach achieved 0.86/0.93 sensitivity/specificity for the binary classification task of HC versus PD. Additional validation on data from 157 subjects (131 HC/26 PD with a total contribution of 76.528 accelerometer and 18.069 typing sessions) with self-reported health status (HC or PD), resulted in area under curve of 0.87, with sensitivity/specificity of 0.92/0.69 and 0.60/0.92 at the operating points of highest sensitivity or specificity, respectively. Our findings suggest that the proposed method can be used as a stepping stone towards the development of an accessible PD screening tool that will passively monitor the subject-smartphone interaction for signs of PD and which could be used to reduce the critical gap between disease onset and start of treatment.

Transcutaneous Afferent Patterned Stimulation Therapy Reduces Hand Tremor for One Hour in Essential Tremor Patients

Essential tremor (ET) patients often experience hand tremor that impairs daily activities. Non-invasive electrical stimulation of median and radial nerves in the wrist using a recently developed therapy called transcutaneous afferent patterned stimulation (TAPS) has been shown to provide symptomatic tremor relief in ET patients and improve patients’ ability to perform functional tasks, but the duration of tremor reduction is unknown. In this single-arm, open-label study, fifteen ET patients performed four hand tremor-specific tasks (postural hold, spiral drawing, finger-to-nose reach, and pouring) from the Fahn-Tolosa-Marin Clinical Rating Scale (FTM-CRS) prior to, during, and 0, 30, and 60 min following TAPS. At each time point, tremor severity was visually rated according to the FTM-CRS and simultaneously measured by wrist-worn accelerometers. The duration of tremor reduction was assessed using (1) improvement in the mean FTM-CRS score across all four tasks relative to baseline, and (2) reduction in accelerometer-measured tremor power relative to baseline for each task. Patients were labeled as having at least 60 min of therapeutic benefit from TAPS with respect to each specified metric if all three (i.e., 0, 30, and 60 min) post-therapy measurements were better than that metric’s baseline value. The mean FTM-CRS scores improved for at least 60 min beyond the end of TAPS for 80% (12 of 15, p = 4.6e–9) of patients. Similarly, for each assessed task, tremor power improved for at least 60 min beyond the end of TAPS for over 70% of patients. The postural hold task had the largest reduction in tremor power (median 5.9-fold peak reduction in tremor power) and had at least 60 min of improvement relative to baseline beyond the end of TAPS therapy for 73% (11 of 15, p = 9.8e–8) of patients. Clinical ratings of tremor severity were correlated to simultaneously recorded accelerometer-measured tremor power (r = 0.33–0.76 across the four tasks), suggesting tremor power is a valid, objective tremor assessment metric that can be used to track tremor symptoms outside the clinic. These results suggest TAPS can provide reductions in upper limb tremor symptoms for at least 1 h post-therapy in some patients, which may improve patients’ ability to perform tasks of daily living.

Local Vibrational Therapy for Essential Tremor Reduction: A Clinical Study

Background and objectives: tremor is an unintentional and rhythmic movement of any part of the body that is a typical symptom of Essential Tremor (ET). ET impairs the quality of life of patients and is treated with pharmacotherapy. We investigated the tremor reduction efficacy of an innovative vibrational medical device (IMD) in ET patients. Materials and Methods: we conducted a prospective, single-center, single-arm, pragmatic study in ET patients with an extended safety study to evaluate the efficacy and safety of the Vilim Ball-a local hand-arm vibration device that produces vibrations in the frequency range of 8-18 Hz and amplitude from 0 to 2 mm. The primary endpoint was the decrease in the power spectrum after device use. The secondary endpoints were safety outcomes. Results: In total, 17 patients with ET were included in the main study, and no patients withdrew from the main study. The tremor power spectrum (m²/s³ Hz) was lower after the device use, represented as the mean (standard deviation): 0.106 (0.221); median (Md) 0.009 with the interquartile range; IQR, 0.087 vs. 0.042 (0.078); Md = 0.009 with the IQR 0.012; Wilcoxon signed-rank test V = 123; and p = 0.027. Seven patients reported that vibrational therapy was not effective. Two patients reported an increase in tremor after using the device. In the extended safety study, we included 51 patients: 31 patients with ET and 20 with Parkinsonian tremor, where 48 patients reported an improvement in tremor symptoms and 49 in function. No serious adverse events were reported, while two patients in the Parkinsonian tremor group reported a lack of efficacy of the proposed medical device. Conclusions: the device reduces essential tremor in some patients and is safe to use in ET.

Evaluation of Wearable Sensor Devices in Parkinson's Disease: A Review of Current Status and Future Prospects

Parkinson's disease (PD) decreases the quality of life of the affected individuals. The incidence of PD is expected to increase given the growing aging population. Motor symptoms associated with PD render the patients unable to self-care and function properly. Given that several drugs have been developed to control motor symptoms, highly sensitive scales for clinical evaluation of drug efficacy are needed. Among such scales, the objective and continuous evaluation of wearable devices is increasingly utilized by clinicians and patients. Several electronic technologies have revolutionized the clinical monitoring of PD development, especially its motor symptoms. Here, we review and discuss the recent advances in the development of wearable devices for bradykinesia, tremor, gait, and myotonia. Our aim is to capture the experiences of patients and clinicians, as well as expand our understanding on the application of wearable technology. In so-doing, we lay the foundation for further research into the use of wearable technology in the management of PD.

Quantification of tremor severity with a mobile tremor pen

BACKGROUND

An objective evaluation of tremor severity is necessary to document the course of disease, the efficacy of treatment, or interventions in clinical trials. Most available objective quantification devices are complex, immobile, or not validated.

NEW METHOD

We used the TREMITAS-System that comprises a pen-shaped sensor for tremor quantification. The Power of Main Peak and the Total Power were used as surrogate markers for tremor amplitude. Tremor severity was assessed by the TREMITAS-System and relevant subscores of the MDS-UPDRS and TETRAS rating scales in 14 patients with Parkinson's disease (PD) and 16 patients with Essential tremor (ET) off and on therapy. We compared tremor amplitudes assessed during wearable and hand-held constellations.

RESULTS

We found significant correlations between tremor amplitudes captured by TREM and tremor severity assessed by the MDS-UPDRS in PD (r = 0.638-0.779) and the TETRAS in ET (r = 0.597-0. 704) off and on therapy. The TREMITAS-System captured the L-Dopa-induced improvement of tremor in PD patients (p = 0.027). Tremor amplitudes did not differ between the handheld and wearable constellation (p > 0.05).

COMPARISON WITH EXISTING METHODS

We confirm the results of previous studies using inertial based sensors that tremor severity and drug-induced changes of tremor severity can be quantified using inertial based sensors. The assessment of tremor amplitudes was not influenced by using a handheld or wearable constellation.

CONCLUSIONS

The TREMITAS-System can be used to quantify rest tremor in PD and postural tremor in ET and is capable of detecting clinically relevant changes in tremor in clinical and research settings.

Effect of eccentric-based rehabilitation on hand tremor intensity in Parkinson disease

BACKGROUND AND PURPOSE

Hand tremor is a disturbing yet sometimes resistant symptom in persons with Parkinson disease (PD). Although many exercise regimens for these people have gained attention in recent years, the effect of resistance training and especially eccentric training on parkinsonian tremor is still uncertain. This study was conducted to investigate the precise effect of upper limb eccentric training on hand tremor in PD.

METHODS

In this randomized controlled trial, a consecutive sample of 21 persons with PD recruited from general hospitals went through 6 weeks of upper limb pure eccentric training as the intervention group (n = 11) or no additional exercise during this period as the control group (n = 10). Resting and postural tremor amplitudes were measured with the cellphone-based accelerometer.

RESULTS

Comparing hand tremor amplitudes before and after the trial showed a significant reduction in resting tremor amplitude in the intervention group after exercise sessions (p < 0.05) while detecting no changes in the control group during 6 weeks of study. Meanwhile, postural tremor amplitude remained unchanged in both groups.

Developing a smartphone application, triaxial accelerometer-based, to quantify static and dynamic balance deficits in patients with cerebellar ataxias

BACKGROUND

Cerebellar ataxia is characterized by difficulty in the planning of movement and lack of anticipatory postural adjustments, which can result in deficits of balance. Being able to have quantitative measurements in clinical practice, to detect any improvements on balance resulting from new rehabilitation treatments or experimental drugs is very important.

AIM

The purpose of this study was to develop an application (APP) able to assess static and dynamic balance in patients with cerebellar ataxias (CA). The APP that works by a wearable device (smartphone) placed at the breastbone level and immobilized by an elastic band, measures the body sway by means of a triaxial accelerometer.

METHODS

We investigated 40 CA patients and 80 healthy subjects. All patients were clinically evaluated using the "Berg Balance Scale" (BBS) and the "Scale for the Assessment and Rating of Ataxia" (SARA). Balance impairment was quantitatively assessed using a validated static balance evaluating systems, i.e., Techno-body Pro-Kin footboard. All participants underwent static and dynamic balance assessments using the new APP.

RESULTS

We observed a strong correlation between the APP measurements and the score obtained with the BBS, SARA, and Pro-Kin footboard. The intra-rater reliability and the test-retest reliability of the APP measurements, estimated by intraclass correlation coefficient, were excellent. The standard error of measurement and the minimal detectable change were small. No learning effect was observed.

CONCLUSIONS

We can state that the APP is an easy, reliable, and valid evaluating system to quantify the trunk sway in a static position and during the gait.

Automated Systems Based on Wearable Sensors for the Management of Parkinson's Disease at Home: A Systematic Review

BACKGROUND

Parkinson's disease is a common neurodegenerative pathology that significantly influences quality of life (QoL) of people affected. The increasing interest and development in telemedicine services and internet of things technologies aim to implement automated smart systems for remote assistance of patients. The wide variability of Parkinson's disease in the clinical expression, as well as in the symptom progression, seems to address the patients' care toward a personalized therapy.

OBJECTIVES

This review addresses automated systems based on wearable/portable devices for the remote treatment and management of Parkinson's disease. The idea is to obtain an overview of the telehealth and automated systems currently developed to address the impairments due to the pathology to allow clinicians to improve the quality of care for Parkinson's disease with benefits for patients in QoL.

DATA SOURCES

The research was conducted within three databases: IEEE Xplore^®, Web of Science^®, and PubMed Central^®, between January 2008 and September 2017.

STUDY ELIGIBILITY CRITERIA

Accurate exclusion criteria and selection strategy were applied to screen the 173 articles found.

RESULTS

Ultimately, 55 articles were fully evaluated and included in this review. Divided into three categories, they were automated systems actually tested at home, implemented mobile applications for Parkinson's disease assessment, or described a telehealth system architecture.

CONCLUSION

This review would provide an exhaustive overview of wearable systems for the remote management and automated assessment of Parkinson's disease, taking into account the reliability and acceptability of the implemented technologies.

Could Wearable and Mobile Technology Improve the Management of Essential Tremor?

Essential tremor (ET) is the most common movement disorder. Individuals exhibit postural and kinetic tremor that worsens over time and patients may also exhibit other motor and non-motor symptoms. While millions of people are affected by this disorder worldwide, several barriers impede an optimal clinical management of symptoms. In this paper, we discuss the impact of ET on patients and review major issues to the optimal management of ET; from the side-effects and limited efficacy of current medical treatments to the limited number of people who seek treatment for their tremor. Then, we propose seven different areas within which mobile and wearable technology may improve the clinical management of ET and review the current state of research in these areas.

Evidence assessing the diagnostic performance of medical smartphone apps: a systematic review and exploratory meta-analysis

OBJECTIVE

The number of mobile applications addressing health topics is increasing. Whether these apps underwent scientific evaluation is unclear. We comprehensively assessed papers investigating the diagnostic value of available diagnostic health applications using inbuilt smartphone sensors.

METHODS

Systematic Review-MEDLINE, Scopus, Web of Science inclusive Medical Informatics and Business Source Premier (by citation of reference) were searched from inception until 15 December 2016. Checking of reference lists of review articles and of included articles complemented electronic searches. We included all studies investigating a health application that used inbuilt sensors of a smartphone for diagnosis of disease. The methodological quality of 11 studies used in an exploratory meta-analysis was assessed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the reporting quality with the 'STAndards for the Reporting of Diagnostic accuracy studies' (STARD) statement. Sensitivity and specificity of studies reporting two-by-two tables were calculated and summarised.

RESULTS

We screened 3296 references for eligibility. Eleven studies, most of them assessing melanoma screening apps, reported 17 two-by-two tables. Quality assessment revealed high risk of bias in all studies. Included papers studied 1048 subjects (758 with the target conditions and 290 healthy volunteers). Overall, the summary estimate for sensitivity was 0.82 (95 % CI 0.56 to 0.94) and 0.89 (95 %CI 0.70 to 0.97) for specificity.

CONCLUSIONS

The diagnostic evidence of available health apps on Apple's and Google's app stores is scarce. Consumers and healthcare professionals should be aware of this when using or recommending them.

PROSPERO REGISTRATION NUMBER

42016033049.

How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review

Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology.

OBJECTIVES

This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring).

DATA SOURCES

The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016.

STUDY ELIGIBILITY CRITERIA

Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers.

RESULTS

Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.

Automatic Classification of Tremor Severity in Parkinson's Disease Using a Wearable Device

Although there is clinical demand for new technology that can accurately measure Parkinsonian tremors, automatic scoring of Parkinsonian tremors using machine-learning approaches has not yet been employed. This study aims to fill this gap by proposing machine-learning algorithms as a way to predict the Unified Parkinson’s Disease Rating Scale (UPDRS), which are similar to how neurologists rate scores in actual clinical practice. In this study, the tremor signals of 85 patients with Parkinson’s disease (PD) were measured using a wrist-watch-type wearable device consisting of an accelerometer and a gyroscope. The displacement and angle signals were calculated from the measured acceleration and angular velocity, and the acceleration, angular velocity, displacement, and angle signals were used for analysis. Nineteen features were extracted from each signal, and the pairwise correlation strategy was used to reduce the number of feature dimensions. With the selected features, a decision tree (DT), support vector machine (SVM), discriminant analysis (DA), random forest (RF), and k-nearest-neighbor (kNN) algorithm were explored for automatic scoring of the Parkinsonian tremor severity. The performance of the employed classifiers was analyzed using accuracy, recall, and precision, and compared to other findings in similar studies. Finally, the limitations and plans for further study are discussed.

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.

Quantification of Hand Motor Symptoms in Parkinson's Disease: A Proof-of-Principle Study Using Inertial and Force Sensors

This proof-of-principle study describes the methodology and explores and demonstrates the applicability of a system, existing of miniature inertial sensors on the hand and a separate force sensor, to objectively quantify hand motor symptoms in patients with Parkinson’s disease (PD) in a clinical setting (off- and on-medication condition). Four PD patients were measured in off- and on- dopaminergic medication condition. Finger tapping, rapid hand opening/closing, hand pro/supination, tremor during rest, mental task and kinetic task, and wrist rigidity movements were measured with the system (called the PowerGlove). To demonstrate applicability, various outcome parameters of measured hand motor symptoms of the patients in off- vs. on-medication condition are presented. The methodology described and results presented show applicability of the PowerGlove in a clinical research setting, to objectively quantify hand bradykinesia, tremor and rigidity in PD patients, using a single system. The PowerGlove measured a difference in off- vs. on-medication condition in all tasks in the presented patients with most of its outcome parameters. Further study into the validity and reliability of the outcome parameters is required in a larger cohort of patients, to arrive at an optimal set of parameters that can assist in clinical evaluation and decision-making.

Validity and reliability of smartphone applications for clinical assessment of the neuromusculoskeletal system

INTRODUCTION

Clinicians increasingly use smartphone medical applications. There is no evidence to support the validity and reliability of applications used to assess the neuromusculoskeletal system. The aim of this study was to systematically review the quality of studies as well as the validity and reliability of using a smartphone as a clinical assessment tool for the neuromusculoskeletal system. Areas covered: PubMed, CINAHL and Embase were searched. A manual search was also conducted. Additionally, forward snowballing of relevant articles was performed in Scopus and Web of Science. Two reviewers independently selected the articles, extracted the data using a standardized form and assessed the articles quality based on a scoring system Expert commentary: Thirty-four articles were found eligible and were categorized into four groups: Range of Motion (ROM), posture and deformity, tremors and reflexes, and gait and mobility. Only the ROM category supported the validity and reliability of using smartphone applications as assessment tools. Regarding quality assessment scores, the articles in ROM and posture and deformity categories ranged from poor to good quality, whereas those in the tremors and reflexes and gait and mobility categories had poor quality.

Continuous Monitoring of Essential Tremor Using a Portable System Based on Smartwatch

INTRODUCTION

Essential tremor (ET) shows amplitude fluctuations throughout the day, presenting challenges in both clinical and treatment monitoring. Tremor severity is currently evaluated by validated rating scales, which only provide a timely and subjective assessment during a clinical visit. Motor sensors have shown favorable performances in quantifying tremor objectively.

METHODS

A new highly portable system was used to monitor tremor continuously during daily lives. It consists of a smartwatch with a triaxial accelerometer, a smartphone, and a remote server. An experiment was conducted involving eight ET patients. The average effective data collection time per patient was 26 (±6.05) hours. Fahn-Tolosa-Marin Tremor Rating Scale (FTMTRS) was adopted as the gold standard to classify tremor and to validate the performance of the system. Quantitative analysis of tremor severity on different time scales is validated.

RESULTS

Significant correlations were observed between neurologist's FTMTRS and patient's FTMTRS auto-assessment scores (r = 0.84; p = 0.009), between the device quantitative measures and the scores from the standardized assessments of neurologists (r = 0.80; p = 0.005) and patient's auto-evaluation (r = 0.97; p = 0.032), and between patient's FTMTRS auto-assessment scores day-to-day (r = 0.87; p < 0.001). A graphical representation of four patients with different degrees of tremor was presented, and a representative system is proposed to summarize the tremor scoring at different time scales.

CONCLUSION

This study demonstrates the feasibility of prolonged and continuous monitoring of tremor severity during daily activities by a highly portable non-restrictive system, a useful tool to analyze efficacy and effectiveness of treatment.

24/7 Live Stream Telemedicine Home Treatment Service for Parkinson's Disease Patients

Background

Treatment of advanced-stage idiopathic Parkinson's disease (PD) is a demanding challenge, and in Germany, medication regimen adjustments are often made during inpatient stays. Admissions often follow an acute worsening of symptoms and functioning. In order to reduce long and expensive inpatient stays, and to provide more frequent consultations, a 24/7 live stream telemedicine home treatment service was established.

Methods

A pilot study was conducted in which laptops were distributed to 50 patients for 1 year to see whether such a service was feasible (in terms of patient participation and compliance) and whether this intervention affected the patient's condition, measured in UPDRS, Mini-Mental Status Examination (MMSE), 39-item Parkinson's Disease Questionnaire (PDQ39), and H & Y Scale.

Results

Seventy-two percent (36) of the patients were compliant and did not experience technical issues. Patients lived, on average, 198 ± 183 km away from the specialist clinic. In total, 264 video conversations took place with 6.9 ± 7.2 (0-29) calls per patient. We found a significant improvement in PDQ39 scores, but not in UPDRS, MMSE, or H & Y scores, at 1 year.

Conclusions

Our data shows that 24/7 live stream telemedicine is feasible and can help to improve quality of life. However, a detailed preliminary review of the patient's willingness to use such a service should be made to obtain the best results. Improvement of the technical setup and network coverage would facilitate an improved service and increase efficiency.

Smartphone-based evaluation of parkinsonian hand tremor: quantitative measurements vs clinical assessment scores

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.

Developing a Tool for Remote Digital Assessment of Parkinson's Disease

Background

The natural fluctuation of motor symptoms of Parkinson's disease (PD) makes judgement of any change challenging and the use of clinical scales such as the International Parkinson and Movement Disorder Society (MDS)-UPDRS imperative. Recently developed commodity mobile communication devices, such as smartphones, could possibly be used to assess motor symptoms in PD patients in a convenient way with low cost. We provide the first report on the development and testing of stand-alone software for mobile devices that could be used to assess both tremor and bradykinesia of PD patients.

Methods

We assessed motor symptoms with a custom-made smartphone application in 14 patients and compared the results with their MDS-UPDRS scores.

Results

We found significant correlation between five subscores of MDS-UPDRS (rest tremor, postural tremor, pronation-supination, leg agility, and finger tapping) and eight parameters of the data collected with the smartphone.

Conclusions

These results provide evidence as a proof of principle that smartphones could be a useful tool to objectively assess motor symptoms in PD in clinical and experimental settings.

A Smartphone-Based Tool for Assessing Parkinsonian Hand Tremor

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.

Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic region is an established therapy for advanced Parkinson's disease (PD). However, patients often require time-intensive post-operative management to balance their coupled stimulation and medication treatments. Given the large and complex parameter space associated with this task, we propose that clinical decision support systems (CDSS) based on machine learning algorithms could assist in treatment optimization.

OBJECTIVE

Develop a proof-of-concept implementation of a CDSS that incorporates patient-specific details on both stimulation and medication.

METHODS

Clinical data from 10 patients, and 89 post-DBS surgery visits, were used to create a prototype CDSS. The system was designed to provide three key functions: (1) information retrieval; (2) visualization of treatment, and; (3) recommendation on expected effective stimulation and drug dosages, based on three machine learning methods that included support vector machines, Naïve Bayes, and random forest.

RESULTS

Measures of medication dosages, time factors, and symptom-specific pre-operative response to levodopa were significantly correlated with post-operative outcomes (P < 0.05) and their effect on outcomes was of similar magnitude to that of DBS. Using those results, the combined machine learning algorithms were able to accurately predict 86% (12/14) of the motor improvement scores at one year after surgery.

CONCLUSIONS

Using patient-specific details, an appropriately parameterized CDSS could help select theoretically optimal DBS parameter settings and medication dosages that have potential to improve the clinical management of PD patients.

Using the Accelerometers Integrated in Smartphones to Evaluate Essential Tremor

Background/Aims: Evaluation of tremor constitutes a crucial step from the diagnosis to the initial treatment and follow-up of patients with essential tremor. The severity of tremor can be evaluated using clinical rating scales, accelerometry, or electrophysiology. Clinical scores are subjectively given, may be affected by intra- and interevaluator variations due to different experience, delays between consultations, and subtle changes in tremor severity. Existing medical devices are not routinely used: they are expensive, time-consuming, not easily accessible. We aimed at showing that a smartphone application using the accelerometers embedded in smartphones is effective for quantifying the tremor of patients presenting with essential tremor. Methods: We developed a free iPhone/iPod application, Itremor, and evaluated different parameters on 8 patients receiving deep brain stimulation of the ventral intermediate nucleus of the thalamus: average and maximum accelerations, time above 1 g of acceleration, peak frequency, typical magnitude of tremor, for postural and action tremors, on and off stimulation. Results: We demonstrated good correlations between the parameters measured with Itremor and clinical score in all conditions. Itremor evaluation enabled higher discriminatory power and degree of reproducibility than clinical scores. Conclusion: Itremor can be used for routine objective evaluation of essential tremor, and may facilitate adjustment of the treatment. © 2015 S. Karger AG, Basel.

The treatment of tremor

Tremor is a hyperkinetic movement disorder characterized by rhythmic oscillations of one or more body parts. It can be disabling and may impair quality of life. Various etiological subtypes of tremor are recognized, with essential tremor (ET) and Parkinsonian tremor being the most common. Here we review the current literature on tremor treatment regarding ET and head and voice tremor, as well as dystonic tremor, orthostatic tremor, tremor due to multiple sclerosis (MS) or lesions in the brainstem or thalamus, neuropathic tremor, and functional (psychogenic) tremor, and summarize main findings. Most studies are available for ET and only few studies specifically focused on other tremor forms. Controlled trials outside ET are rare and hence most of the recommendations are based on a low level of evidence. For ET, propranolol and primidone are considered drugs of first choice with a mean effect size of approximately 50 % tremor reduction. The efficacy of topiramate is also supported by a large double-blind placebo-controlled trial, while other drugs have less supporting evidence. With a mean effect size of about 90 % deep brain stimulation in the nucleus ventralis intermedius or the subthalamic nucleus may be the most potent treatment; however, there are no controlled trials and it is reserved for severely affected patients. Dystonic limb tremor may respond to anticholinergics. Botulinum toxin improves head and voice tremor. Gabapentin and clonazepam are often recommended for orthostatic tremor. MS tremor responds only poorly to drug treatment. For patients with severe MS tremor, thalamic deep brain stimulation has been recommended. Patients with functional tremor may benefit from antidepressants and are best be treated in a multidisciplinary setting. Several tremor syndromes can already be treated with success. But new drugs specifically designed for tremor treatment are needed. ET is most likely covering different entities and their delineation may also improve treatment. Modern study designs and long-term studies are needed.