Rapid tremor frequency assessment with the iPhone accelerometer

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.

Digital Phenotyping in Clinical Neurology

Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.

Accuracy of Smartphone Video for Contactless Measurement of Hand Tremor Frequency

Background

Computer vision can measure movement from video without the time and access limitations of hospital accelerometry/electromyography or the requirement to hold or strap a smartphone accelerometer.

Objective

To compare computer vision measurement of hand tremor frequency from smartphone video with a gold standard measure accelerometer.

Methods

A total of 37 smartphone videos of hands, at rest and in posture, were recorded from 15 participants with tremor diagnoses (9 Parkinson's disease, 5 essential tremor, 1 functional tremor). Video pixel movement was measured using the computing technique of optical flow, with contemporaneous accelerometer recording. Fast Fourier transform and Bland-Altman analysis were applied. Tremor amplitude was scored by 2 clinicians.

Results

Bland-Altman analysis of dominant tremor frequency from smartphone video compared with accelerometer showed excellent agreement: 95% limits of agreement -0.38 Hz to +0.35 Hz. In 36 of 37 videos (97%), there was <0.5 Hz difference between computer vision and accelerometer measurement. There was no significant correlation between the level of agreement and tremor amplitude.

Conclusion

The study suggests a potential new, contactless point-and-press measure of tremor frequency within standard clinical settings, research studies, or telemedicine.

QUANTITATIVE ANALYSIS OF ESSENTIAL TREMOR DURING CLINICAL SPIRAL DRAWING TASK USING GYRO SENSORS

The spiral drawing task is a representative clinical test used to assess essential tremor. Many clinicians have used spirography to assess through visual rating of the drawings. We quantitatively investigated the clinical characteristics of action tremors during spiral drawing tasks using 3-axis gyro sensors. Eighteen essential tremor patients with sensors attached to their forefinger, dorsum of the hand, and forearm participated in a spiral drawing test. As quantitative variables, the average speed, maximum frequency and maximum power were derived from the gyro sensor signals. The average speed and maximum power were also logarithmically transformed. Two-way repeated measures analysis of variance (ANOVA) with tremor direction and joint (attachment location) as independent factors was performed to evaluate the main and interactive effects of direction and joint. Also, Spearman’s correlation analysis was performed to evaluate the relationship between the quantitative variables and visual rating scores. The average speed showed a significant main effect of direction ([Formula: see text]). Logarithmic variables showed interactive effects ([Formula: see text]) as well as main effects ([Formula: see text]). The interactive effects were attributed to no significant difference among the joints particularly in the roll direction ([Formula: see text]). The logarithmic variables of roll direction were significantly greater than that of the other directions ([Formula: see text]). Also, the quantitative variables were moderately correlated with visual rating scores ([Formula: see text], [Formula: see text]). These results indicate that kinetic tremor during spiral drawing mainly appears in the roll direction and may contribute to the determination of sensor location and direction for continuous monitoring of patients with essential tremor.

An Experimental Study on the Validity and Reliability of a Smartphone Application to Acquire Temporal Variables during the Single Sit-to-Stand Test with Older Adults

Smartphone sensors have often been proposed as pervasive measurement systems to assess mobility in older adults due to their ease of use and low-cost. This study analyzes a smartphone-based application’s validity and reliability to quantify temporal variables during the single sit-to-stand test with institutionalized older adults. Forty older adults (20 women and 20 men; 78.9 ± 8.6 years) volunteered to participate in this study. All participants performed the single sit-to-stand test. Each sit-to-stand repetition was performed after an acoustic signal was emitted by the smartphone app. All data were acquired simultaneously with a smartphone and a digital video camera. The measured temporal variables were stand-up time and total time. The relative reliability and systematic bias inter-device were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. In contrast, absolute reliability was assessed using the standard error of measurement and coefficient of variation (CV). Inter-device concurrent validity was assessed through correlation analysis. The absolute percent error (APE) and the accuracy were also calculated. The results showed excellent reliability (ICC = 0.92–0.97; CV = 1.85–3.03) and very strong relationships inter-devices for the stand-up time (r = 0.94) and the total time (r = 0.98). The APE was lower than 6%, and the accuracy was higher than 94%. Based on our data, the findings suggest that the smartphone application is valid and reliable to collect the stand-up time and total time during the single sit-to-stand test with older adults.

Quantification of tremor using consumer product accelerometry is feasible in patients with essential tremor and Parkinson's disease: a comparative study

Background

To quantify pharmacological effects on tremor in patients with essential tremor (ET) or Parkinson’s Disease (PD), laboratory-grade accelerometers have previously been used. Over the last years, consumer products such as smartphones and smartwatches have been increasingly applied to measure tremor in an easy way. However, it is unknown how the technical performance of these consumer product accelerometers (CPAs) compares to laboratory-grade accelerometers (LGA). This study was performed to compare the technical performance of CPAs with LGA to measure tremor in patients with Parkinson’s Disease (PD) and essential tremor (ET).

Methods

In ten patients with PD and ten with ET, tremor peak frequency and corresponding amplitude were measured with 7 different CPAs (Apple iPhone 7, Apple iPod Touch 5, Apple watch 2, Huawei Nexus 6P, Huawei watch, mbientlabMetaWear (MW) watch, mbientlab MW clip) and compared to a LGA (Biometrics ACL300) in resting and extended arm position.

Results

Both in PD and ET patients, the peak frequency of CPAs did not significantly differ from the LGA in terms of limits of agreement. For the amplitude at peak frequency, only the iPhone and MW watch performed comparable to the LGA in ET patients, while in PD patients all methods performed comparable except for the iPod Touch and Huawei Nexus. Amplitude was higher when measured with distally-located CPAs (Clip, iPhone, iPod) compared with proximally-located CPAs (all watches). The variability between subjects was higher than within subjects for frequency (25.1% vs. 13.4%) and amplitude measurement (331% vs. 53.6%). Resting arm position resulted in lower intra-individual variability for frequency and amplitude (13.4 and 53.5%) compared to extended arm position (17.8 and 58.1%).

Conclusions

Peak frequencies of tremor could be measured with all tested CPAs, with similar performance as LGA. The amplitude measurements appeared to be driven by anatomical location of the device and can therefore not be compared. Our results show that the tested consumer products can be used for tremography, allowing at-home measurements, in particular in studies with a cross-over or intra-individual comparison design using the resting arm position.

Trial registration

This trial was registered in the Dutch Competent Authority (CCMO) database with number NL60672.058.17 on May 30th 2017.

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.

Population-scale hand tremor analysis via anonymized mouse cursor signals

Tremors are a common movement disorder with a spectrum of benign and pathological causes, including neurodegenerative disease, alcohol withdrawal, and physical overexertion. Studies of tremors in clinical practice are limited in size and scope and depend on explicit tracking of tremor characteristics by clinicians. Data drawn from small numbers of patients observed in short-duration sessions pose challenges for understanding the nature and distribution of tremors over a large population. Methods are presented to estimate hand tremors based on anonymized computer mouse cursor movement data collected from millions of users of a web search engine. To determine the feasibility of using this signal for the estimation of the prevalence of tremors over a large population, the characteristics of tremor-like movements are computed and compared against user data that can be interpreted as self-reports, the findings of published clinical studies, and a target selection study where participants self-report hand tremors and known causes. The results demonstrate significant alignment between estimated tremors and both self-reports and clinical findings. Those with cursor tremor events are more likely to report tremor-related search interests. Variations in cursor tremor quantity and cursor tremor frequency with demographics mirror those from clinical studies. Distributions of cursor tremor frequencies vary as expected for different medical conditions. Overall, the study finds evidence for the validity of harnessing anonymized mouse cursor motion as a population-scale tremor sensor for epidemiologic studies. Feasible future applications include opt-in services for screening and for monitoring the progression of illness.

Accurate and affordable assessment of physiological and pathological tremor in rodents using the accelerometer of a smartphone

Tremor is a common symptom for the most prevalent neurological disorders, including essential tremor, spinal cord injury, multiple sclerosis, or Parkinson’s disease. Despite the devastating effects of tremor on life quality, available treatments are few and unspecific. Because of the need for specific and costly devices, tremor is rarely quantified by laboratories studying motor control without a genuine interest in trembling. We present a simple, reliable, and affordable method aimed at monitoring tremor in rodents, with an accuracy comparable to that of expensive, commercially available equipment. We took advantage of the accelerometer integrated in modern mobile phones working with operating systems capable of running downloaded apps. By fixing a smartphone to a cage suspended by rubber bands, we were able to detect faint vibrations of the cage. With a mouse in the cage, we showed that the acceleration signals on two horizontal axes were sufficient for the detection of physiological tremor and harmaline-induced tremor. We discuss the advantages and limitations of our method.

NEW & NOTEWORTHY The majority of patients suffering from neurological disorders suffer from tremor that severely disrupts their life quality. Because of the high cost of specific scientific equipment, tremor is rarely quantified by laboratories working on motor behavior. For this reason, the potential anti-tremor effect of most compounds tested in animals remains unknown. We describe an affordable technique that will allow any laboratory to measure tremor accurately with a smartphone.

Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research

Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis and remote objective monitoring of an individual's disease symptoms, both in clinical care and in research. Nonetheless, such digital health technologies have yet to widely demonstrate value in clinical research due to insufficient data interpretability and lack of regulatory acceptance. Metadata, i.e., data that accompany and describe the primary data, can be utilized to better understand the context of the sensor data and can assist in data management, data sharing, and subsequent data analysis. The need for data and metadata standards for digital health technologies has been raised in academic and industry research communities and has also been noted by regulatory authorities. Therefore, to address this unmet need, we here propose a metadata set that reflects regulatory guidelines and that can serve as a conceptual map to (1) inform researchers on the metadata they should collect in digital health studies, aiming to increase the interpretability and exchangeability of their data, and (2) direct standard development organizations on how to extend their existing standards to incorporate digital health technologies. The proposed metadata set is informed by existing standards pertaining to clinical trials and medical devices, in addition to existing schemas that have supported digital health technology studies. We illustrate this specifically in the context of Parkinson's disease, as a model for a wide range of other chronic conditions for which remote monitoring would be useful in both care and science. We invite the scientific and clinical research communities to apply the proposed metadata set to ongoing and planned research. Where the proposed metadata fall short, we ask users to contribute to its ongoing revision so that an adequate degree of consensus can be maintained in a rapidly evolving technology landscape.

Identification of Parkinson's Disease Utilizing a Single Self-recorded 20-step Walking Test Acquired by Smartphone's Inertial Measurement Unit

Parkinson's disease (PD) is a degenerative and long-term disorder of the central nervous system, which often causes motor symptoms, e.g., tremor, rigidity, and slowness. Currently, the diagnosis of PD is based on patient history and clinical examination. Technology-derived decision support systems utilizing, for example, sensor-rich smartphones can facilitate more accurate PD diagnosis. These technologies could provide less obtrusive and more comfortable remote symptom monitoring. The recent studies showed that motor symptoms of PD can reliably be detected from data gathered via smartphones. The current study utilized an open-access dataset named "mPower" to assess the feasibility of discriminating PD from non-PD by analyzing a single self-administered 20-step walking test. From this dataset, 1237 subjects (616 had PD) who were age and gender matched were selected and classified into PD and non-PD categories. Linear acceleration (ACC) and gyroscope (GYRO) were recorded by built-in sensors of smartphones. Walking bouts were extracted by thresholding signal magnitude area of the ACC signals. Features were computed from both ACC and GYRO signals and fed into a random forest classifier of size 128 trees. The classifier was evaluated deploying 100-fold cross-validation and provided an accumulated accuracy rate of 0.7 after 10k validations. The results show that PD and non-PD patients can be separated based on a single short-lasting self-administered walking test gathered by smartphones' built-in inertial measurement units.

Activity-aware essential tremor evaluation using deep learning method based on acceleration data

BACKGROUND

Essential tremor (ET), one of the most common neurological disorders is typically evaluated with validated rating scales which only provide a subjective assessment during a clinical visit, underestimating the fluctuations tremor during different daily activities. Motion sensors have shown favorable performances in both quantifying tremor and voluntary human activity recognition (HAR).

OBJECTIVE

To create an automated system of a reference scale using motion sensors supported by deep learning algorithms to accurately rate ET severity during voluntary activities, and to propose an IOTA based blockchain application to share anonymously tremor data.

METHOD

A smartwatch-based tremor monitoring system was used to collect motion data from 20 subjects while they were doing standard tasks. Two neurologists rated ET by Fahn-Tolosa Marin Tremor Rating Scale (FTMTRS). Supported by deep learning techniques, activity classification models (ACMs) and tremor evaluation models (TEMs) were created and algorithms were implemented, to distinguish voluntary human activities and evaluate tremor severity respectively.

RESULT

A practical application example showed that the proposed ACMs can classify six typical activities with high accuracy (89.73%-98.84%) and the results produced by the TEMs are significantly correlated with the FTMTRS ratings of two neurologists (r₁ = 0.92, p₁ = 0.008; r₂ = 0.93, p₂ = 0.007).

CONCLUSION

This study demonstrated that motion sensor data, supported by deep learning algorithms, can be used to classify human activities and evaluate essential tremor severity during different activities.

Automated Quality Control for Sensor Based Symptom Measurement Performed Outside the Lab

The use of wearable sensing technology for objective, non-invasive and remote clinimetric testing of symptoms has considerable potential. However, the accuracy achievable with such technology is highly reliant on separating the useful from irrelevant sensor data. Monitoring patient symptoms using digital sensors outside of controlled, clinical lab settings creates a variety of practical challenges, such as recording unexpected user behaviors. These behaviors often violate the assumptions of clinimetric testing protocols, where these protocols are designed to probe for specific symptoms. Such violations are frequent outside the lab and affect the accuracy of the subsequent data analysis and scientific conclusions. To address these problems, we report on a unified algorithmic framework for automated sensor data quality control, which can identify those parts of the sensor data that are sufficiently reliable for further analysis. Combining both parametric and nonparametric signal processing and machine learning techniques, we demonstrate that across 100 subjects and 300 clinimetric tests from three different types of behavioral clinimetric protocols, the system shows an average segmentation accuracy of around 90%. By extracting reliable sensor data, it is possible to strip the data of confounding factors in the environment that may threaten reproducibility and replicability.

Quantifying Motor Impairment in Movement Disorders

Until recently the assessment of many movement disorders has relied on clinical rating scales that despite careful design are inherently subjective and non-linear. This makes accurate and truly observer-independent quantification difficult and limits the use of sensitive parametric statistical methods. At last, devices capable of measuring neurological problems quantitatively are becoming readily available. Examples include the use of oculometers to measure eye movements and accelerometers to measure tremor. Many applications are being developed for use on smartphones. The benefits include not just more accurate disease quantification, but also consistency of data for longitudinal studies, accurate stratification of patients for entry into trials, and the possibility of automated data capture for remote follow-up. In this mini review, we will look at movement disorders with a particular focus on Parkinson's disease, describe some of the limitations of existing clinical evaluation tools, and illustrate the ways in which objective metrics have already been successful.

Targeting KCa1.1 Channels with a Scorpion Venom Peptide for the Therapy of Rat Models of Rheumatoid Arthritis

Fibroblast-like synoviocytes (FLSs) are a key cell type involved in rheumatoid arthritis (RA) progression. We previously identified the KCa1.1 potassium channel (Maxi-K, BK, Slo 1, KCNMA1) as a regulator of FLSs and found that KCa1.1 inhibition reduces disease severity in RA animal models. However, systemic KCa1.1 block causes multiple side effects. In this study, we aimed to determine whether the KCa1.1 β1-3-specific venom peptide blocker iberiotoxin (IbTX) reduces disease severity in animal models of RA without inducing major side effects. We used immunohistochemistry to identify IbTX-sensitive KCa1.1 subunits in joints of rats with a model of RA. Patch-clamp and functional assays were used to determine whether IbTX can regulate FLSs through targeting KCa1.1. We then tested the efficacy of IbTX in ameliorating disease in two rat models of RA. Finally, we determined whether IbTX causes side effects including incontinence or tremors in rats, compared with those treated with the small-molecule KCa1.1 blocker paxilline. IbTX-sensitive subunits of KCa1.1 were expressed by FLSs in joints of rats with experimental arthritis. IbTX inhibited KCa1.1 channels expressed by FLSs from patients with RA and by FLSs from rat models of RA and reduced FLS invasiveness. IbTX significantly reduced disease severity in two rat models of RA. Unlike paxilline, IbTX did not induce tremors or incontinence in rats. Overall, IbTX inhibited KCa1.1 channels on FLSs and treated rat models of RA without inducing side effects associated with nonspecific KCa1.1 blockade and could become the basis for the development of a new treatment of RA.

Time measurement characterization of stand-to-sit and sit-to-stand transitions by using a smartphone

The aim of this study is to analyze a common method to measure the acceleration of a daily activity pattern by using a smartphone. In this sense, a numerical approach is proposed to transform the relative acceleration signal, recorded by a triaxial accelerometer, into an acceleration referred to an inertial reference. The integration of this acceleration allows to determine the velocity and position with respect to an inertial reference. Two different kinematic parameters are suggested to characterize the profile of the velocity during the sit-to-stand and stand-to-sit transitions for Parkinson and control subjects. The results show that a dimensionless kinematic parameter, which is linked to the time of sit-to-stand and stand-to-sit transitions, has the potential to differentiate between Parkinson and control subjects.

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.

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.

Clinical validation of a precision electromagnetic tremor measurement system in participants receiving deep brain stimulation for essential tremor

Tremor is characterized commonly through subjective clinical rating scales. Accelerometer-based techniques for objective tremor measurement have been developed in the past, yet these measures are usually presented as an unintuitive dimensionless index without measurement units. Here we have developed a tool (TREMBAL) to provide quantifiable and objective measures of tremor severity using electromagnetic motion tracking. We aimed to compare TREMBAL's objective measures with clinical tremor ratings and determine the test-retest reliability of our technique. Eight participants with ET receiving deep brain stimulation (DBS) therapy were consented. Tremor was simultaneously recorded using TREMBAL and video during DBS adjustment. After each adjustment, participants performed a hands-outstretched task (for postural tremor) and a finger-nose task (for kinetic tremor). Video recordings were de-identified, randomized, and shown to a panel of movement disorder specialists to obtain their ratings. Regression analysis and Pearson's correlations were used to determine agreement between datasets. Subsets of the trial were repeated to assess test-retest reliability. Tremor amplitude and velocity measures were in close agreement with mean clinical ratings (r  >  0.90) for both postural and kinetic tremors. Test-retest reliability for both translational and rotational components of tremor showed intra-class correlations  >0.80. TREMBAL assessments showed that tremor gradually improved with increasing DBS therapy-this was also supported by clinical observation. TREMBAL measurements are a sensitive, objective and reliable assessment of tremor severity. This tool may have application in clinical trials and in aiding automated optimization of deep brain stimulation.

Parkinson's disease hand tremor detection system for mobile application

Parkinson's disease currently affects millions of people worldwide and is steadily increasing. Many symptoms are associated with this disease, including rest tremor, bradykinesia, stiffness or rigidity of the extremities and postural instability. No cure is currently available for Parkinson's disease patients; instead most medications are for treatment of symptoms. This treatment depends on the quantification of these symptoms such as hand tremor. This work proposes a new system for mobile phone applications. The system is based on measuring the acceleration from the Parkinson's disease patient's hand using a mobile cell phone accelerometer. Recordings from 21 Parkinson's disease patients and 21 healthy subjects were used. These recordings were analysed using a two level wavelet packet analysis and features were extracted forming a feature vector of 12 elements. The features extracted from the 42 subjects were classified using a neural networks classifier. The results obtained showed an accuracy of 95% and a Kappa coefficient of 90%. These results indicate that a cell phone accelerometer can accurately detect and record rest tremor in Parkinson's disease patients.

Smartphone-based diagnostic for preeclampsia: an mHealth solution for administering the Congo Red Dot (CRD) test in settings with limited resources

OBJECTIVE

Morbidity and mortality due to preeclampsia in settings with limited resources often results from delayed diagnosis. The Congo Red Dot (CRD) test, a simple modality to assess the presence of misfolded proteins in urine, shows promise as a diagnostic and prognostic tool for preeclampsia. We propose an innovative mobile health (mHealth) solution that enables the quantification of the CRD test as a batch laboratory test, with minimal cost and equipment.

METHODS

A smartphone application that guides the user through seven easy steps, and that can be used successfully by non-specialized personnel, was developed. After image acquisition, a robust analysis runs on a smartphone, quantifying the CRD test response without the need for an internet connection or additional hardware. In the first stage, the basic image processing algorithms and supporting test standardizations were developed using urine samples from 218 patients. In the second stage, the standardized procedure was evaluated on 328 urine specimens from 273 women. In the third stage, the application was tested for robustness using four different operators and 94 altered samples.

RESULTS

In the first stage, the image processing chain was set up with high correlation to manual analysis (z-test P < 0.001). In the second stage, a high agreement between manual and automated processing was calculated (Lin's concordance coefficient ρc = 0.968). In the last stage, sources of error were identified and remedies were developed accordingly. Altered samples resulted in an acceptable concordance with the manual gold-standard (Lin's ρc = 0.914).

CONCLUSION

Combining smartphone-based image analysis with molecular-specific disease features represents a cost-effective application of mHealth that has the potential to fill gaps in access to health care solutions that are critical to reducing adverse events in resource-poor settings.

Tracking the Evolution of Smartphone Sensing for Monitoring Human Movement

Advances in mobile technology have led to the emergence of the “smartphone”, a new class of device with more advanced connectivity features that have quickly made it a constant presence in our lives. Smartphones are equipped with comparatively advanced computing capabilities, a global positioning system (GPS) receivers, and sensing capabilities (i.e., an inertial measurement unit (IMU) and more recently magnetometer and barometer) which can be found in wearable ambulatory monitors (WAMs). As a result, algorithms initially developed for WAMs that “count” steps (i.e., pedometers); gauge physical activity levels; indirectly estimate energy expenditure and monitor human movement can be utilised on the smartphone. These algorithms may enable clinicians to “close the loop” by prescribing timely interventions to improve or maintain wellbeing in populations who are at risk of falling or suffer from a chronic disease whose progression is linked to a reduction in movement and mobility. The ubiquitous nature of smartphone technology makes it the ideal platform from which human movement can be remotely monitored without the expense of purchasing, and inconvenience of using, a dedicated WAM. In this paper, an overview of the sensors that can be found in the smartphone are presented, followed by a summary of the developments in this field with an emphasis on the evolution of algorithms used to classify human movement. The limitations identified in the literature will be discussed, as well as suggestions about future research directions.

Field Tests of a Tractor Rollover Detection and Emergency Notification System

The objective of this research was to assess the feasibility of a rollover detection and emergency notification system for farm tractors using field tests. The emergency notification system was developed based on a tractor stability model and implemented on a mobile electronic device with the iOS operating system. A complementary filter was implemented to combine the data from the accelerometer and gyroscope sensors to improve their accuracies in calculating the roll and pitch angles and the roll and pitch rates. The system estimates a stability index value during tractor operation, displays feedback messages when the stability index is lower than a preset threshold value, and transmits emergency notification messages when an overturn happens. Ten tractor rollover tests were conducted on a field track. The developed system successfully monitored the stability of the tractor during all of the tests. The iOS application was able to detect rollover accidents and transmit emergency notifications in the form of a phone call and email when an accident was detected. The system can be a useful tool for training and education in safe tractor operation. The system also has potential for stability monitoring and emergency notification of other on-road and off-road motorized vehicles.

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.

Potential of a smartphone as a stress-free sensor of daily human behaviour

Behaviour is one of the most powerful objective signals that connotes psychological functions regulated by neuronal network systems. This study searched for simple behaviours using smartphone sensors with three axes for measuring acceleration, angular speed and direction. We used quantitative analytic methodology of pattern recognition for work contexts, individual workers and seasonal effects in our own longitudinally recorded data. Our 13 laboratory members were involved in the care of common marmosets and domestic chicks, which lived in separate rooms. They attached a smartphone to their front waist-belts during feeding and cleaning in five care tasks. Behavioural characteristics such as speed, acceleration and azimuth, pitch, and roll angles were monitored. Afterwards, participants noted subjective scores of warmth sensation and work efficiency. The multivariate time series behavioral data were characterized by the subjective scores and environmental factors such as room temperature, season, and humidity, using the linear mixed model. In contrast to high-precision but stress-inducing sensors, the mobile sensors measuring daily behaviours allowed us to quantify the effects of the psychological states and environmental factors on the behavioural traits.

Smart watch accelerometry for analysis and diagnosis of tremor

BACKGROUND

Distinguishing the postural re-emergent tremor of Parkinson disease from essential tremor can be difficult clinically. Use of accelerometry to aid diagnosis is limited to laboratory settings. We sought to record and differentiate these tremors using a smart watch device in an outpatient clinic.

NEW METHOD

41 patients were enrolled. Recordings were made with a smart watch device on the predominantly affected hand (all patients), and simultaneously with an analog accelerometer (10 patients) with hands at rest and outstretched. Tremor peak frequency, peak power, and power of the first four harmonics was calculated and compared between the two devices. Mean power at the first four harmonics was calculated and used to classify tremor as parkinsonian or essential. Test characteristics were calculated to compare the device and clinical diagnoses.

RESULTS

Mean harmonic peak power was both highly sensitive and specific for distinction of Parkinson disease postural tremor from essential tremor with an optimal threshold for our sample (sensitivity 90.9%, 95% CI 58.7-99.8%; specificity 100%, 95% CI 76.8-100%; Cohen's kappa=0.91, SE=0.08).

COMPARISON WITH EXISTING METHODS

The smart watch and analog devices had nearly perfect concordance of peak frequency and proportional harmonic power. The smart watch recordings in clinic took 3-6 min.

CONCLUSIONS

A smart watch device can provide accurate and diagnostically relevant information about postural tremor. Its portability and ease of use could help translate such techniques into routine clinic use or to the community.

Quantitative assessment of levodopa-induced dyskinesia using automated motion sensing technology

The objective was to capture levodopa-induced dyskinesia (LID) in patients with Parkinson's disease (PD) using body-worn motion sensors. Dopaminergic treatment in PD can induce abnormal involuntary movements, including choreatic dyskinesia (brief, rapid, irregular movements). Adjustments in medication to reduce LID often sacrifice control of motor symptoms, and balancing this tradeoff poses a significant challenge for management of advanced PD. Fifteen PD subjects with known LID were recruited and instructed to perform two stationary motor tasks while wearing a compact wireless motion sensor unit positioned on each hand over the course of a levodopa dose cycle. Videos of subjects performing the motor tasks were later scored by expert clinicians to assess global dyskinesia using the modified Abnormal Involuntary Rating Scale (m-AIMS). Kinematic features were extracted from motion data in different frequency bands (1-3Hz and 3-8Hz) to quantify LID severity and to distinguish between LID and PD tremor. Receiver operator characteristic analysis was used to determine thresholds for individual features to detect the presence of LID. A sensitivity of 0.73 and specificity of 1.00 were achieved. A neural network was also trained to output dyskinesia severity on a 0 to 4 scale, similar to the m-AIMS. The model generalized well to new data (coefficient of determination= 0.85 and mean squared error= 0.3). This study demonstrated that hand-worn motion sensors can be used to assess global dyskinesia severity independent of PD tremor over the levodopa dose cycle.

An accelerometry-based study of lower and upper limb tremor in Parkinson's disease

Over the past two decades, several studies have aimed to quantify the kinetic properties of tremor with primary focus on the upper limbs. However, there is a lack of investigation into the properties of tremor in the lower limbs. The objective of this preliminary study was to investigate the properties of oscillatory movement, at rest and in posture, in both the upper and lower limbs of Parkinson's disease (PD) patients with clinically undetectable to modest rest/postural tremor and healthy controls. PD patients (N = 16) and controls (N = 8) were examined clinically by a movement disorders specialist and oscillatory movements in all four extremities were evaluated using a portable biaxial accelerometer. While tremor intensity and frequency did not differ between groups, the intraindividual variability of rest and postural tremor frequency in the dexterity-dominant lower limb was lower in people living with PD than in healthy adults. Additionally, rest tremor frequency was discrepant between upper and lower limbs in PD. Our work introduces the possibility that minute variations in lower limb movements, which are imperceptible upon expert clinical exam, can be used to differentiate a diseased sample from a healthy one. These preliminary findings suggest that additional work using objective tremor measurement may improve our understanding of lower limb motor dysfunction in PD and lead to the refinement of current, and the development of new, metrics to enhance early diagnosis, differential diagnosis, and symptom quantification.

Engagement with automated patient monitoring and self-management support calls: experience with a thousand chronically ill patients

BACKGROUND

Patient self-care support via Interactive Voice Response (IVR) can improve disease management. However, little is known about the factors affecting program engagement.

METHODS

We compiled data on IVR program engagement for 1173 patients with: heart failure, depression, diabetes, or cancer who were followed for 28,962 person-weeks. Patients in programs for diabetes or depression (N=727) had the option of participating along with an informal caregiver who received electronic feedback based on the patient's IVR assessments. Analyses focused on factors associated with completing weekly IVR calls.

RESULTS

Patients were on average 61 years old, 37% had at most a high school education, and 48% reported incomes of ≤$30,000. Among patients given the option of participating with an informal caregiver, 65% chose to do so. Patients completed 83% of attempted IVR assessments, with rates higher for heart failure (90%) and cancer programs (90%) than for the diabetes (81%) or depression programs (71%) (P<0.001). Among patients in diabetes or depression programs, those opting to have feedback provided to an informal caregiver were more likely to complete assessments [adjusted odds ratio, 1.37; 95% confidence interval, 1.07-1.77]. Older patients had higher call completion rates, even among patients aged 75 years and older. Missed clinic appointments, prior hospitalizations, depression program participation, and poorer mental health were associated with lower completion rates.

CONCLUSIONS

Patients with a variety of chronic conditions will complete IVR self-care support calls regularly. Risk factors for missed IVR calls overlap with those for missed appointments. Involvement of informal caregivers may significantly increase engagement.

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

Introduction: Smart phones are becoming ubiquitous and their computing capabilities are ever increasing. Consequently, more attention is geared toward their potential use in research and medical settings. For instance, their built-in hardware can provide quantitative data for different movements. Therefore, the goal of the current study was to evaluate the capabilities of a standalone smart phone platform to characterize tremor. Results: Algorithms for tremor recording and online analysis can be implemented within a smart phone. The smart phone provides reliable time- and frequency-domain tremor characteristics. The smart phone can also provide medically relevant tremor assessments. Discussion: Smart phones have the potential to provide researchers and clinicians with quantitative short- and long-term tremor assessments that are currently not easily available. Methods: A smart phone application for tremor quantification and online analysis was developed. Then, smart phone results were compared to those obtained simultaneously with a laboratory accelerometer. Finally, results from the smart phone were compared to clinical tremor assessments.

Using mobile phones for activity recognition in Parkinson's patients

Mobile phones with built-in accelerometers promise a convenient, objective way to quantify everyday movements and classify those movements into activities. Using accelerometer data we estimate the following activities of 18 healthy subjects and eight patients with Parkinson’s disease: walking, standing, sitting, holding, or not wearing the phone. We use standard machine learning classifiers (support vector machines, regularized logistic regression) to automatically select, weigh, and combine a large set of standard features for time series analysis. Using cross validation across all samples we are able to correctly identify 96.1% of the activities of healthy subjects and 92.2% of the activities of Parkinson’s patients. However, when applying the classification parameters derived from the set of healthy subjects to Parkinson’s patients, the percent correct lowers to 60.3%, due to different characteristics of movement. For a fairer comparison across populations we also applied subject-wise cross validation, identifying healthy subject activities with 86.0% accuracy and 75.1% accuracy for patients. We discuss the key differences between these populations, and why algorithms designed for and trained with healthy subject data are not reliable for activity recognition in populations with motor disabilities.

How to tackle tremor - systematic review of the literature and diagnostic work-up

Background: Tremor is the most prevalent movement disorder in clinical practice. It is defined as involuntary, rhythmic, oscillatory movements. The diagnostic process of patients with tremor can be laborious and challenging, and a clear, systematic overview of available diagnostic techniques is lacking. Tremor can be a symptom of many diseases, but can also represent a distinct disease entity. Objective: The objective of this review is to give a clear, systematic and step-wise overview of the diagnostic work-up of a patient with tremor. The clinical relevance and value of available laboratory tests in patients with tremor will be explored. Methods: We systematically searched through EMBASE. The retrieved articles were supplemented by articles containing relevant data or provided important background information. Studies that were included investigated the value and/or usability of diagnostic tests for tremor. Results: In most patients, history and clinical examination by an experienced movement disorders neurologist are sufficient to establish a correct diagnosis, and further ancillary examinations will not be needed. Ancillary investigation should always be guided by tremor type(s) present and other associated signs and symptoms. The main ancillary examination techniques currently are electromyography and SPECT imaging. Unfortunately, many techniques have not been studied in large prospective, diagnostic studies to be able to determine important variables like sensitivity and specificity. Conclusion: When encountering a patient with tremor, history, and careful clinical examination should guide the diagnostic process. Adherence to the diagnostic work-up provided in this review will help the diagnostic process of these patients.

A systematic review of healthcare applications for smartphones

BACKGROUND

Advanced mobile communications and portable computation are now combined in handheld devices called "smartphones", which are also capable of running third-party software. The number of smartphone users is growing rapidly, including among healthcare professionals. The purpose of this study was to classify smartphone-based healthcare technologies as discussed in academic literature according to their functionalities, and summarize articles in each category.

METHODS

In April 2011, MEDLINE was searched to identify articles that discussed the design, development, evaluation, or use of smartphone-based software for healthcare professionals, medical or nursing students, or patients. A total of 55 articles discussing 83 applications were selected for this study from 2,894 articles initially obtained from the MEDLINE searches.

RESULTS

A total of 83 applications were documented: 57 applications for healthcare professionals focusing on disease diagnosis (21), drug reference (6), medical calculators (8), literature search (6), clinical communication (3), Hospital Information System (HIS) client applications (4), medical training (2) and general healthcare applications (7); 11 applications for medical or nursing students focusing on medical education; and 15 applications for patients focusing on disease management with chronic illness (6), ENT-related (4), fall-related (3), and two other conditions (2). The disease diagnosis, drug reference, and medical calculator applications were reported as most useful by healthcare professionals and medical or nursing students.

CONCLUSIONS

Many medical applications for smartphones have been developed and widely used by health professionals and patients. The use of smartphones is getting more attention in healthcare day by day. Medical applications make smartphones useful tools in the practice of evidence-based medicine at the point of care, in addition to their use in mobile clinical communication. Also, smartphones can play a very important role in patient education, disease self-management, and remote monitoring of patients.

Assessment of tremor activity in the Parkinson's disease using a set of wearable sensors

Tremor is the most common motor disorder of Parkinson's disease (PD) and consequently its detection plays a crucial role in the management and treatment of PD patients. The current diagnosis procedure is based on subject-dependent clinical assessment, which has a difficulty in capturing subtle tremor features. In this paper, an automated method for both resting and action/postural tremor assessment is proposed using a set of accelerometers mounted on different patient's body segments. The estimation of tremor type (resting/action postural) and severity is based on features extracted from the acquired signals and hidden Markov models. The method is evaluated using data collected from 23 subjects (18 PD patients and 5 control subjects). The obtained results verified that the proposed method successfully: 1) quantifies tremor severity with 87 % accuracy, 2) discriminates resting from postural tremor, and 3) discriminates tremor from other Parkinsonian motor symptoms during daily activities.

Essential tremor quantification during activities of daily living

BACKGROUND

Essential tremor (ET), characterized primarily by postural and kinetic tremor, is typically measured in the clinic with subjective tremor rating scales. These ratings are often used to adjust medications and assess efficacy in clinical trials. However, tremor ratings require the presence of a clinician and do not necessarily capture tremor fluctuations throughout the day during activities of daily living (ADL).

OBJECTIVE

To evaluate the ability of motion sensors to discriminate tremor from voluntary posture and motion, classify tremor as postural or kinetic, and rate tremor severity during standardized tasks and non-standardized activities of daily living.

METHODS

Ten subjects with ET wore motion sensors on the index finger and performed standardized motor tasks from the Washington Heights-Inwood Genetic Study of Essential Tremor (WHIGET) tremor rating scale (wTRS) and non-standardized ADL tasks. Four movement disorder specialists independently rated video segments of the standardized tasks but not the ADL tasks. Quantitative features were extracted from the motion sensors and used to develop mathematical models for predicting rating scores from kinematic data.

RESULTS

The quantitative motion features were highly correlated with wTRS ratings for postural (r = 0.90) and kinetic (r = 0.80) tremors. Mathematical models produced tremor ratings that correlated strongly with clinician ratings of the wTRS tasks (mean r = 0.80) and also produced ADL task ratings that correlated well with the most recent clinician wTRS ratings (mean r = 0.72).

CONCLUSIONS

Recordings from motion sensors can be used to classify tremor as postural or kinetic and quantify tremor severity during both standardized and non-standardized activities.