A novel quantitative method for 3D measurement of Parkinsonian tremor

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.

An In-Laboratory Validity and Reliability Tested System for Quantifying Hand-Arm Tremor in Motions

Despite the advancement of the tremor assessment systems, the current technology still lacks a method that can objectively characterize tremors in relative segmental movements. This paper presents a measurement system, which quantifies multi-degrees-of-freedom coupled relative motions of hand-arm tremor, in terms of joint angular displacement. In-laboratory validity and reliability tests of the system algorithm to provide joint angular displacement was carried out by using the two-degrees-of-freedom tremor simulator with incremental rotary encoder systems installed. The statistical analyses show that the developed system has high validity results and comparable reliability performances using the rotary encoder system as the reference. In the clinical trials, the system was tested on 38 Parkinson's disease patients. The system readings were correlated with the observational tremor ratings of six trained medical doctors. The moderate to very high clinical correlations of the system readings in measuring rest, postural and task-specific tremors add merits to the degree of readiness of the developed tremor measurement system in a routine clinical setting and/or intervention trial for tremor amelioration.

Tremor UPDRS estimation in home environment

In this paper, a method for the assessment of the Unified Parkinson Disease Rating scale (UPDRS) related to tremor is presented. The method described consists of hand resting and posture state detection, tremor detection and tremor quantification based on accelerometer and gyroscope readings from a wrist worn sensor. The initial results on PD patient recordings on home environment indicate the feasibility of the proposed method in monitoring UPDRS tremor in patient home environment.

Quantitative Measurement of Akinesia in Parkinson's Disease

BACKGROUND

There is great interest in developing simple, user-friendly, and inexpensive tools for the quantification and elucidation of motor deficits in patients with Parkinson's disease (PD). These systems could help to monitor the clinical status of patients with PD, to develop better treatments, and to identify individuals who have subtle motor signs that might pass unnoticed in the conventional neurological examination.

METHODS

Mememtum, a smartphone application that allows for the quantification of several parameters of movement, such as regularity, rhythm, and changes in the number of taps while taping with a single finger and with alternating fingers, was developed and then tested in a pilot study in Madrid and in an extensive study in Quito, Ecuador.

RESULTS

Almost all patients could successfully perform single-finger tapping, but approximately 10% of patients with severe parkinsonism had problems taping with alternating fingers. The results revealed changes in the regularity of the pressure applied while tapping and a reduction in the number of taps on the device screen when alternating tapping among patients who had idiopathic PD and vascular parkinsonism compared with controls and individuals who had prediagnostic motor abnormalities of PD.

CONCLUSION

Applications available in smartphones could be used for investigation and treatment of patients with PD, but much research is needed to optimize the ideal parameters to be investigated and the potential usefulness of this technique for patients with PD in different stages of the disease.

Using Portable Transducers to Measure Tremor Severity

BACKGROUND

Portable motion transducers, suitable for measuring tremor, are now available at a reasonable cost. The use of these transducers requires knowledge of their limitations and data analysis. The purpose of this review is to provide a practical overview and example software for using portable motion transducers in the quantification of tremor.

METHODS

Medline was searched via PubMed.gov in December 2015 using the Boolean expression "tremor AND (accelerometer OR accelerometry OR gyroscope OR inertial measurement unit OR digitizing tablet OR transducer)." Abstracts of 419 papers dating back to 1964 were reviewed for relevant portable transducers and methods of tremor analysis, and 105 papers written in English were reviewed in detail.

RESULTS

Accelerometers, gyroscopes, and digitizing tablets are used most commonly, but few are sold for the purpose of measuring tremor. Consequently, most software for tremor analysis is developed by the user. Wearable transducers are capable of recording tremor continuously, in the absence of a clinician. Tremor amplitude, frequency, and occurrence (percentage of time with tremor) can be computed. Tremor amplitude and occurrence correlate strongly with clinical ratings of tremor severity.

DISCUSSION

Transducers provide measurements of tremor amplitude that are objective, precise, and valid, but the precision and accuracy of transducers are mitigated by natural variability in tremor amplitude. This variability is so great that the minimum detectable change in amplitude, exceeding random variability, is comparable for scales and transducers. Research is needed to determine the feasibility of detecting smaller change using averaged data from continuous long-term recordings with wearable transducers.

Clinicians' Tendencies to Under-Rate Parkinsonian Tremors in the Less Affected Hand

The standard assessment method for tremor severity in Parkinson’s disease is visual observation by neurologists using clinical rating scales. This is, therefore, a subjective rating that is dependent on clinical expertise. The objective of this study was to report clinicians’ tendencies to under-rate Parkinsonian tremors in the less affected hand. This was observed through objective tremor measurement with accelerometers. Tremor amplitudes were measured objectively using tri-axis-accelerometers for both hands simultaneously in 53 patients with Parkinson’s disease during resting and postural tremors. The videotaped tremor was rated by neurologists using clinical rating scales. The tremor measured by accelerometer was compared with clinical ratings. Neurologists tended to under-rate the less affected hand in resting tremor when the contralateral hand had severe tremor in Session I. The participating neurologists corrected this tendency in Session II after being informed of it. The under-rating tendency was then repeated by other uninformed neurologists in Session III. Kappa statistics showed high inter-rater agreements and high agreements between estimated scores derived from the accelerometer signals and the mean Clinical Tremor Rating Scale evaluated in every session. Therefore, clinicians need to be aware of this under-rating tendency in visual inspection of the less affected hand in order to make accurate tremor severity assessments.

PERFORM: a system for monitoring, assessment and management of patients with Parkinson's disease

In this paper, we describe the PERFORM system for the continuous remote monitoring and management of Parkinson's disease (PD) patients. The PERFORM system is an intelligent closed-loop system that seamlessly integrates a wide range of wearable sensors constantly monitoring several motor signals of the PD patients. Data acquired are pre-processed by advanced knowledge processing methods, integrated by fusion algorithms to allow health professionals to remotely monitor the overall status of the patients, adjust medication schedules and personalize treatment. The information collected by the sensors (accelerometers and gyroscopes) is processed by several classifiers. As a result, it is possible to evaluate and quantify the PD motor symptoms related to end of dose deterioration (tremor, bradykinesia, freezing of gait (FoG)) as well as those related to over-dose concentration (Levodopa-induced dyskinesia (LID)). Based on this information, together with information derived from tests performed with a virtual reality glove and information about the medication and food intake, a patient specific profile can be built. In addition, the patient specific profile with his evaluation during the last week and last month, is compared to understand whether his status is stable, improving or worsening. Based on that, the system analyses whether a medication change is needed—always under medical supervision—and in this case, information about the medication change proposal is sent to the patient. The performance of the system has been evaluated in real life conditions, the accuracy and acceptability of the system by the PD patients and healthcare professionals has been tested, and a comparison with the standard routine clinical evaluation done by the PD patients' physician has been carried out. The PERFORM system is used by the PD patients and in a simple and safe non-invasive way for long-term record of their motor status, thus offering to the clinician a precise, long-term and objective view of patient's motor status and drug/food intake. Thus, with the PERFORM system the clinician can remotely receive precise information for the PD patient's status on previous days and define the optimal therapeutical treatment.

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.

Clinically deployable Kinesia technology for automated tremor assessment

The objective was to design, build, and assess Kinesia, a wireless system for automated assessment of Parkinson's disease (PD) tremor. The current standard in evaluating PD is the Unified Parkinson's Disease Rating Scale (UPDRS), a qualitative ranking system typically completed during an office visit. Kinesia integrates accelerometers and gyroscopes in a compact patient-worn unit to capture kinematic movement disorder features. Objectively quantifying PD manifestations with increased time resolution should aid in evaluating efficacy of treatment protocols and improve patient management. In this study, PD subjects performed the tremor subset of the UPDRS motor section while wearing Kinesia. Quantitative kinematic features were processed and highly correlated to clinician scores for rest tremor (r(2) = 0.89), postural tremor (r(2) = 0.90), and kinetic tremor (r(2) = 0.69). The quantitative features were used to develop a mathematical model that predicted tremor severity scores for new data with low errors. Finally, PD subjects indicated high clinical acceptance.

Motion analysis in the training and assessment of minimally invasive surgery

Dexterity is a crucial aspect of surgical competence and is considered to be of high priority in the selection of trainees to specialities such as laparoscopic surgery. A motion analysis system, Imperial College Surgical Assessment Device (ICSAD) has undergone validation studies and is sensitive in discriminating surgeons according to their experience. It consists of a signal generator that creates an electromagnetic field in which sensors placed on the surgeon's hands can be detected using a special software. These positional data can be converted to data reflecting the surgeon's dexterity. It is a useful adjunct to training as trainees can be expected to achieve a certain level of proficiency prior to progress. They can also be provided with an objective feedback of their performance. As an assessment method it can be used for credentialing in laparoscopic surgery.

Extraction of sources of tremor in hand movements of patients with movement disorders

This paper proposes an efficient method to acquire sources of tremor in patients with movement disorders based on blind source separation of convolutive mixtures. The extracted sources indicated neural activities that might be generated in the central nervous system. Four patients with essential tremor were tested in a set of movement tasks. Subjects wore a data glove that measured finger movements of the hand. The experimental data were then fed to a convolutive-mixture model, which revealed sources that imbibed in them the tremor frequency components of 2--8 Hz. Time--frequency analysis of these sources might be of potential help to clinicians to devise tasks that can manifest visible tremor from patients.

Integrated digital image and accelerometer measurements of rat locomotor and vibratory behaviour

This study developed a combined IC-type accelerometer and video camera system to simultaneously measure vibration and locomotion activities in rats. A personal computer, adopted as an image frame grabber, was combined with a digital image processing algorithm to measure the precise location of an animal in an experimental cage. An accelerometer-based vibration subsystem, based on an 89C51 single-chip microprocessor, was designed. The acceleration sensor module was attached directly to the shaved back of the rat's body to directly measure the animal's vibration. This module can detect a wide range of vibrations from movements of the entire body to micro-tremors. Along with hardware, this study also proposes novel software for video enhancement and data analysis to calculate the behaviour parameters from recorded movements. In normal mode, three vibration activities (locomotor activity, tremor and twitch) are auto-analyzed every 10 min. The results are saved, and various display, statistical and data organization options are available. The primary merits of this system are the ability to simultaneously record locomotion and vibrational data, the rapid set-up and operation, the low cost, the reduced illumination requirements, the reduction of environmental noise and the high precision. The proposed method will be of interest to researchers in various behavioural, biological and medical fields.

Gravitational artifact in accelerometric measurements of tremor

OBJECTIVE

To illustrate the problem of gravitational artifact in accelerometric recordings of tremor.

METHODS

Gravitational and inertial accelerations were computed for a triaxial accelerometer that was attached to a hand, oscillating vertically about the wrist. Mathematical equations for hand motion were solved with commercial software.

RESULTS

Accelerometer output contains proportionately larger gravitational artifact at lower frequencies of oscillation and when the accelerometer is mounted closer to the wrist. A vertical accelerometer axis contains nearly constant gravitational artifact when the amplitude of wrist oscillation is less than +/-20 degrees. Proportionately large gravitational artifact can occur in accelerometer axes that are perpendicular to the path of motion. Gravitational and inertial oscillations at twice the frequency of wrist oscillation emerge from the equations of motion.

CONCLUSIONS

When the hand is horizontal, the vertical accelerometer axis contains nearly constant gravitational artifact during the recording of most tremors, and high-pass filtering effectively removes this artifact. Gravitational artifact is more problematic when the hand is vertical, as during the measurement of Parkinson rest tremor, particularly if the accelerometer is mounted close to the wrist.

SIGNIFICANCE

Two accelerometers, mounted in parallel, are needed to capture hand rotation in a single plane of motion, free of gravitational artifact.

Quantifying the contribution of arm postural tremor to the outcome of goal-directed pointing task by displacement measures

A method for quantifying the outcome of goal-directed postural pointing was presented and used for relating the tremor output to the oscillations of single arm landmarks. The displacement of reflective markers placed on shoulder, upper arm, forearm, and hand were measured by an optoelectronic motion capture system in nine subjects holding a laser penlight pointed at a target. The high signal-to-noise ratio of the measured displacement series (from 7:1 for shoulder marker to 30:1 for hand marker) demonstrated the feasibility of the proposed system to carry out tremor analysis. The track of the laser emission on the target, reconstructed from penlight displacements, was studied as the outcome of the pointing task. By measuring the length and the dispersion of the path covered by the target track in 30 s, a significant and comparable tremor amplitude was found in vertical and lateral directions. The correlation between target track and arm tremor was stronger for distal than for proximal markers. Spectral and cross-spectral analyses evidenced the presence of a dominant low frequency oscillation (1.5 Hz), along with a high frequency one (5-7 Hz), in the displacement of the target track. These two components were significantly linked to the oscillations of the hand, the forearm and the upper arm but not to those of the shoulder, suggesting that the oscillations in the overall tremor output during the pointing task are mainly generated by the arm segments.

A virtual reality-based system for hand function analysis

The goal of this study was to demonstrate the usability and usefulness of virtual reality technology in assessing hand functions. Ten healthy, non-disabled right-handed adult volunteers were recruited. Each volunteer used a dataglove to insert three-dimensional virtual representations of a cylinder and a prism into the target holes. To verify the reliability of the tests, each subject was retested twice. The performance testing assessed the visual-motor coordination a person needs to achieve a task accurately and within a set time. For each trial, the root mean square (RMS) value of the hand movement trajectory was projected onto the X, Y, and Z axes. This projection enabled us to measure the extent of the genuine, summative displacement of the manipulating hand. The reproducibility of the virtual reality assessment was analyzed using the intraclass correlation (ICC) approach. The total ICC values of 10 subjects demonstrated a high task completion time and RMS on the X and Z axes for the transferring of the prism. However, the values were low for the transferring of the cylinder. Because the individual coefficients of variations (CVs) varied widely in the moving of both the cylinder and the prism, the total (CVs) showed a high reading for the task completion time. Although rehabilitation clinics routinely carry out peg-moving exercises for disabled patients, our model provides a valuable quantitative real time and off-line measure of whole hand functions.

Fuzzy methods in tremor assessment, prediction, and rehabilitation

Tremor is a disabling condition for a large segment of population, mainly elderly. To the present date, there are no adequate tools to diagnose and help rehabilitation of subjects with tremor, but recently there is a tremendous surge of interest in the research in the field. We report on the use of fuzzy methods in applications for rehabilitation, namely in tremor diagnosing and control. We synthesize our results regarding the characterization of the tremor by means of nonlinear dynamics techniques and fuzzy logic, and the prediction of tremor movements in view of rehabilitation purposes. Based on linear and nonlinear analysis of tremor, and using fuzzy aggregation, the fusing of tremor parameters in global functional disabling factors is proposed. Nonlinear dynamic parameters, namely correlation dimension and Lyapunov exponent is used in order to improve the assessment of tremor. The benefits of the fuzzy fused tremor parameters rely on more complete and accurate assessment of the functional impairment and on improved feedback for rehabilitation, based on the fused parameters of the tremor. Further steps in rehabilitation may require external muscular control. In turn, the control of tremor by electrical stimulation requires movement prediction. Several neural and neuro-fuzzy predictors are compared and a neuro-fuzzy predictor is presented, allowing us five-step ahead prediction, with an RMS error of the order of 10%. The benefits of the neuro-fuzzy predictor are good prediction capability, versatility, and apparently a high robustness to individual variations of the tremor. The reported research, which extended over several years and included development of sensors, equipment, and software, has been aimed to development of products. The results may also open new ways in tremor rehabilitation.