Using the Accelerometers Integrated in Smartphones to Evaluate Essential Tremor

Intelligent devices for assessing essential tremor: a comprehensive review

Essential tremor (ET) stands as the most prevalent movement disorder, characterized by rhythmic and involuntary shaking of body parts. Achieving an accurate and comprehensive assessment of tremor severity is crucial for effectively diagnosing and managing ET. Traditional methods rely on clinical observation and rating scales, which may introduce subjective biases and hinder continuous evaluation of disease progression. Recent research has explored new approaches to quantifying ET. A promising method involves the use of intelligent devices to facilitate objective and quantitative measurements. These devices include inertial measurement units, electromyography, video equipment, and electronic handwriting boards, and more. Their deployment enables real-time monitoring of human activity data, featuring portability and efficiency. This capability allows for more extensive research in this field and supports the shift from in-lab/clinic to in-home monitoring of ET symptoms. Therefore, this review provides an in-depth analysis of the application, current development, potential characteristics, and roles of intelligent devices in assessing ET.

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.

Tremor evaluation using smartphone accelerometry in standardized settings

Tremor can be highly incapacitating in everyday life and typically fluctuates depending on motor state, medication status as well as external factors. For tremor patients being treated with deep-brain stimulation (DBS), adapting the intensity and pattern of stimulation according the current needs therefore has the potential to generate better symptomatic relief. We here describe a procedure for how patients independently could perform self-tests in their home to generate sensor data for on-line adjustments of DBS parameters. Importantly, the inertia sensor technology needed exists in any standard smartphone, making the procedure widely accessible. Applying this procedure, we have characterized detailed features of tremor patterns displayed by both Parkinson’s disease and essential tremor patients and directly compared measured data against both clinical ratings (Fahn-Tolosa-Marin) and finger-attached inertia sensors. Our results suggest that smartphone accelerometry, when used in a standardized testing procedure, can provide tremor descriptors that are sufficiently detailed and reliable to be used for closed-loop control of DBS.

Quantification of Head Tremors in Medical Conditions: A Comparison of Analyses Using a 2D Video Camera and a 3D Wireless Inertial Motion Unit

This study compares two methods to quantify the amplitude and frequency of head movements in patients with head tremor: one based on video-based motion analysis, and the other using a miniature wireless inertial magnetic motion unit (IMMU). Concomitant with the clinical assessment of head tremor severity, head linear displacements in the frontal plane and head angular displacements in three dimensions were obtained simultaneously in forty-nine patients using one video camera and an IMMU in three experimental conditions while sitting (at rest, counting backward, and with arms extended). Head tremor amplitude was quantified along/around each axis, and head tremor frequency was analyzed in the frequency and time-frequency domains. Correlation analysis investigated the association between the clinical severity of head tremor and head linear and angular displacements. Our results showed better sensitivity of the IMMU compared to a 2D video camera to detect changes of tremor amplitude according to examination conditions, and better agreement with clinical measures. The frequency of head tremor calculated from video data in the frequency domain was higher than that obtained using time-frequency analysis and those calculated from the IMMU data. This study provides strong experimental evidence in favor of using an IMMU to quantify the amplitude and time-frequency oscillatory features of head tremor, especially in medical conditions.

Movement markers of schizophrenia: a detailed analysis of patients' gait patterns

Motor abnormalities occur in the majority of persons with schizophrenia but are generally neglected in clinical care. Psychiatric diagnostics fail to include quantifiable motor variables and few assessment tools examine full-body movement. We assessed full-body movement during gait of 20 patients and 20 controls with motion capture technology, symptom load (PANSS, BPRS) and Neurological Soft Signs (NSS). In a data-driven analysis, participants' motion patterns were quantified and compared between groups. Resulting movement markers (MM) were correlated with the clinical assessment. We identified 16 quantifiable MM of schizophrenia. While walking, patients and controls display significant differences in movement patterns related to posture, velocity, regularity of gait as well as sway, flexibility and integration of body parts. Specifically, the adjustment of body sides, limbs and movement direction were affected. The MM remain significant when controlling for medication load. They are systematically related to NSS. Results add assessment tools, analysis methods as well as theory-independent MM to the growing body of research on motor abnormalities in schizophrenia.

Tremor assessment using smartphone sensor data and fuzzy reasoning

Background

Tremor severity assessment is an important step for the diagnosis and treatment decision-making of essential tremor (ET) patients. Traditionally, tremor severity is assessed by using questionnaires (e.g., ETRS and QUEST surveys). In this work we assume the possibility of assessing tremor severity using sensor data and computerized analyses. The goal of this work is to assess severity of tremor objectively, to be better able to asses improvement in ET patients due to deep brain stimulation or other treatments.

Methods

We collect tremor data by strapping smartphones to the wrists of ET patients. The resulting raw sensor data is then pre-processed to remove any artifact due to patient’s intentional movement. Finally, this data is exploited to automatically build a transparent, interpretable, and succinct fuzzy model for the severity assessment of ET. For this purpose, we exploit pyFUME, a tool for the data-driven estimation of fuzzy models. It leverages the FST-PSO swarm intelligence meta-heuristic to identify optimal clusters in data, reducing the possibility of a premature convergence in local minima which would result in a sub-optimal model. pyFUME was also combined with GRABS, a novel methodology for the automatic simplification of fuzzy rules.

Results

Our model is able to assess tremor severity of patients suffering from Essential Tremor, notably without the need for subjective questionnaires nor interviews. The fuzzy model improves the mean absolute error (MAE) metric by 78–81% compared to linear models and by 71–74% compared to a model based on decision trees.

Conclusion

This study confirms that tremor data gathered using the smartphones is useful for the constructing of machine learning models that can be used to support the diagnosis and monitoring of patients who suffer from Essential Tremor. The model produced by our methodology is easy to inspect and, notably, characterized by a lower error with respect to approaches based on linear models or decision trees.

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.

The use of smartphone in measuring stance and gait patterns in patients with orthostatic tremor

Orthostatic tremor (OT) is a rare movement disorder characterized by a fast tremor (13–18 Hz) in the lower extremities during stance. Patients with OT typically complain of instability while standing/walking. However, due to the geographical limitation, the standing instability or gait problems in patients with OT cannot be assessed and monitored frequently. The increasing popularity of using smartphone-based accelerometers could be a solution to eliminate this limitation. This study examined the feasibility of using smartphone-based accelerometers to identify the changes in body movement in different standing and locomotor tasks. Twenty patients with OT and seven healthy controls were consented to participate in this study. Subjects stood with eyes open or eyes closed for 20 seconds. They also performed four different locomotor tasks (normal walking, tandem walk, walking on an elevated surface, and obstacle negotiation). When performed different locomotor tasks, patients with OT had a larger acceleration of body movement than controls in the medial-lateral direction (tandem walk: p = 0.026, walking on an elevated surface: p = 0.002, and stepping over the obstacle: p = 0.028). Patients with OT had smaller acceleration of body movement than controls while standing with eyes open in the vertical direction (p = 0.012), in the anterior-posterior direction (p = 0.013) and in the medial-lateral direction (p = 0.011). This study provides objective evidence of balance instability in patients with OT not only while standing but also during different challenging locomotor tasks by using smartphone-based accelerometers.

Essential tremor quantification based on the combined use of a smartphone and a smartwatch: The NetMD study

BACKGROUND

The use of wearable technology is an emerging field of research in movement disorders. This paper introduces a clinical study to evaluate the feasibility, clinical correlation and reliability of using a system based in smartwatches to quantify tremor in essential tremor (ET) patients and check its acceptance as clinical monitoring tool.

NEW METHOD

The system is based on a commercial smartwatch and an Android smartphone. An investigational Android application controls the process of recording raw data from the smartwatch three-dimensional gyroscopes. Thirty-four ET patients were consecutively enrolled in the experiments and assessed along one year. Arm tremor was videofilmed and scored using the Fahn-Tolosa-Marin Tremor Rating Scale (FTM-TRS). Tremor intensity was quantified with the root mean square of angular velocity measured in the patients' wrists.

RESULTS

Eighty-two assessments with smartwatches were performed. Spearman's correlation coefficients (ρ) between clinical tremor (FTM-TRS) scores and smartwatch measures for tremor intensity were 0.590 at rest; ρ = 0.738 in steady posture; ρ = 0.189 in finger-to-nose maneuvers; and ρ = 0.652 in pouring water task. Smartwatch reliability was checked by intraclass realiability coefficients: 0.85, 0.95, 0.91, 0.95 respectively. Most of patients showed good acceptance of the system.

COMPARISON WITH EXISTING METHOD(S)

This commodity hardware contributes to quantify tremor objectively in a consulting-room by customized Android smart devices as clinical monitoring tool.

CONCLUSIONS

The NetMD system for tremor analysis is feasible, well-correlated with clinical scores, reliable and well-accepted by patients to tremor follow-up. Therefore, it could be an option to objectively quantify tremor in ET patients during their regular follow-up.

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.

The clinical and prognostic value of motor abnormalities in psychosis, and the importance of instrumental assessment

Motor abnormalities comprise several clinical signs intrinsic to psychosis. Critically, these features are of prognostic value in individuals at-risk for psychosis, and for those in early stages of psychotic disorders. Motor abnormalities such as tremor, rigidity, and neurological soft signs often go unrecognized. Currently, advances in this area are limited by a paucity of theoretical conceptions categorizing or linking these behaviours to underlying neurobiology affected in psychosis. However, emerging technological advances have significantly improved the ability to detect and assess motor abnormalities with objective instruments in a timely and reliable manner. Further, converging evidence has laid the groundwork for theoretically and empirically derived categorization and conceptualization. This review summarizes these advances, stressing the importance of motor abnormalities for understanding vulnerability across different stages of psychosis and introducing these innovative instrumental approaches. Patients, researchers and clinicians will benefit from these new developments, as better assessment aids the development of targeted interventions to ultimately improve the care for individuals experiencing psychosis.

Intraoperative acceleration measurements to quantify improvement in tremor during deep brain stimulation surgery

Deep brain stimulation (DBS) surgery is extensively used in the treatment of movement disorders. Nevertheless, methods to evaluate the clinical response during intraoperative stimulation tests to identify the optimal position for the implantation of the chronic DBS lead remain subjective. In this paper, we describe a new, versatile method for quantitative intraoperative evaluation of improvement in tremor with an acceleration sensor that is mounted on the patient's wrist during surgery. At each anatomical test position, the improvement in tremor compared to the initial tremor is estimated on the basis of extracted outcome measures. This method was tested on 15 tremor patients undergoing DBS surgery in two centers. Data from 359 stimulation tests were acquired. Our results suggest that accelerometric evaluation detects tremor changes more sensitively than subjective visual ratings. The effective stimulation current amplitudes identified from the quantitative data (1.1 ± 0.8 mA) are lower than those identified by visual evaluation (1.7 ± 0.8 mA) for similar improvement in tremor. Additionally, if these data had been used to choose the chronic implant position of the DBS lead, 15 of the 26 choices would have been different. These results show that our method of accelerometric evaluation can potentially improve DBS targeting.

TREMOR12: An Open-Source Mobile App for Tremor Quantification

BACKGROUND

Evaluating the effect of treatment of tremor is mostly performed with clinical rating scales. Mobile applications facilitate a more rapid, objective, and quantitative evaluation of treatment effect. Existing mobile apps do not offer raw data access, which limits algorithm development.

OBJECTIVE

To develop a novel open-source mobile app for tremor quantification.

METHODS

TREMOR12 is an open-source mobile app that samples acceleration, rotation, rotation speed, and gravity, each in 3 axes and time-stamped in a frequency up to 100 Hz. The raw measurement data can be exported as a comma-separated value file for further analysis in the TREMOR12P data processing module. The app was evaluated with 3 patients suffering from essential tremor, who were between 55 and 71 years of age.

RESULTS

This proof-of-concept study shows that the TREMOR12 app is able to detect and register tremor characteristics such as acceleration, rotation, rotation speed, and gravity in a simple and nonburdensome way. The app is compatible with current regulatory oversight by the European Union (MEDDEV regulations) and the Food and Drug Administration (FDA) guidance on mobile medical applications.

CONCLUSION

TREMOR12 offers low-cost tremor quantification for research purposes and algorithm development, and may help to improve treatment evaluation.

Assessment of Head Tremor with Accelerometers Versus Gyroscopic Transducers

Background

Accelerometers and gyroscopes are used commonly in the assessment of hand tremor, but their validity in the assessment of head tremor has not been studied. We hypothesized that gyroscopy would be superior to accelerometry because head tremor is rotational motion, and gyroscopes record rotational motion, free of gravitational artifact. We also hypothesized a strong logarithmic relationship between 0 to 4-point tremor ratings and the transducer measures of tremor amplitude, similar to those previously reported for hand tremor.

Methods

Head tremor was recorded for 1 minute in each of the five head positions used in the Essential Tremor Rating Assessment Scale, using a triaxial accelerometer and triaxial gyroscope mounted at the vertex of the head. Mean and maximum 3-second burst displacement tremor and rotation tremor were computed by spectral analysis. The minimum detectable change for the transducers was estimated using the residual mean squared error from repeated-measures analysis of variance.

Results

Tremor displacement and rotation (T) were logarithmically related to tremor ratings (tremor rating score; TRS): log(T) = α TRS + β, where α ≈ 0.47 for displacement and ≈0.64 for rotation, and β ≈ -1.8 and -1.4. Tremor ratings correlated more strongly with gyroscopy (r = 0.83-0.87) than with accelerometry (r = 0.71-0.75). Minimum detectable change (percent reduction) was approximately 66% of the baseline geometric mean.

Conclusions

Gyroscopic transducers are superior to accelerometry for assessment of head tremor. Both measures of head tremor are logarithmically related to tremor ratings. The minimum detectable change of the transducer measures is comparable to those previously reported for hand tremor.