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

López-Blanco, Roberto; Velasco, Miguel Ángel; Méndez-Guerrero, Antonio; Romero, Juan Pablo; Castillo, M. Dolores del; Serrano, J. Ignacio; Benito‐León, Julián; Bermejo‐Pareja, Félix; Rocón, Eduardo

doi:10.1016/j.jneumeth.2018.02.015

Cited by 32 publications

(43 citation statements)

References 32 publications

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“…However, few studies had shown that these technologies could be used to evaluate essential tremor (ET) [ 74 , 75 ] and tremor in Parkinson's disease [ 76 ]. In particular, a moderate-good correlation between MWD output (worn in the most tremulous wrist) and clinical tremor scores (at the Fahn-Tolosa-Marin tremor rating scale) was found in people with ET [ 74 ]. In the same group of patients, during the finger-to-nose maneuver (for kinetic tremor detection) no correlation was found between MWD output and clinical scores.…”

Section: Motion Wearable Devices (Mwd)mentioning

confidence: 99%

The Role of Wearable Devices in Multiple Sclerosis

Sparaco

Lavorgna

Conforti

et al. 2018

Multiple Sclerosis International

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Multiple sclerosis (MS) is the most common neurological disorder in young adults. The prevalence of walking impairment in people with MS (pwMS) is estimated between 41% and 75%. To evaluate the walking capacity in pwMS, the patient reported outcomes (PROs) and performance-based tests (i.e., the 2-minute walk test, the 6-minute walk test, the Timed 25-Foot Walk Test, the Timed Up and Go Test, and the Six Spot Step Test) could be used. However, some studies point out that the results of both performance-based tests and objective measures (i.e., by accelerometer) could not reflect patient reports of walking performance and impact of MS on daily life. This review analyses different motion sensors embedded in smartphones and motion wearable device (MWD) that can be useful to measure free-living walking behavior, to evaluate falls, fatigue, sedentary lifestyle, exercise, and quality of sleep in everyday life of pwMS. Caveats and limitations of MWD such as variable accuracy, user adherence, power consumption and recharging, noise susceptibility, and data management are discussed as well.

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Section: Motion Wearable Devices (Mwd)mentioning

confidence: 99%

The Role of Wearable Devices in Multiple Sclerosis

Sparaco

Lavorgna

Conforti

et al. 2018

Multiple Sclerosis International

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“…Recent reviews can be found in [7] – [9] . Additionally, some groups have used machine learning algorithms to quantify tremor [10] , [11] , and many groups have evaluated the potential of commercial off-the-shelf smart watches and smart phones [12] – [15] . However, much of the algorithm development has focused on rest tremor, which is common to Parkinson’s disease (PD).…”

Section: Introductionmentioning

confidence: 99%

“…Previous algorithm designs have largely ignored these properties of tremor that are specific to action tremor common to ET [12] . While some recent work has shown a correlation between a portable smartwatch tremor recording device and both the Fahn-Tolosa-Marin Tremor Rating Scale [12] , [13] and the Washington Heights-Inwood Genetic Study of Essential Tremor Tremor Rating Scale [24] , these algorithms did not account for these properties of the action tremor common to ET.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Tremor in Essential Tremor Using Inertial Sensors—Validation of an Algorithm

McGurrin

McNames

et al. 2021

IEEE J. Transl. Eng. Health Med.

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Background Assessment of essential tremor is often done by a trained clinician who observes the limbs during different postures and actions and subsequently rates the tremor. While this method has been shown to be reliable, the inter- and intra-rater reliability and need for training can make the use of this method for symptom progression difficult. Many limitations of clinical rating scales can potentially be overcome by using inertial sensors, but to date many algorithms designed to quantify tremor have key limitations. Methods We propose a novel algorithm to characterize tremor using inertial sensors. It uses a two-stage approach that 1) estimates the tremor frequency of a subject and only quantifies tremor near that range; 2) estimates the tremor amplitude as the portion of signal power above baseline activity during recording, allowing tremor estimation even in the presence of other activity; and 3) estimates tremor amplitude in physical units of translation (cm) and rotation (°), consistent with current tremor rating scales. We validated the algorithm technically using a robotic arm and clinically by comparing algorithm output with data reported by a trained clinician administering a tremor rating scale to a cohort of essential tremor patients. Results Technical validation demonstrated rotational amplitude accuracy better than ±0.2 degrees and position amplitude accuracy better than ±0.1 cm. Clinical validation revealed that both rotation and position components were significantly correlated with tremor rating scale scores. Conclusion We demonstrate that our algorithm can quantify tremor accurately even in the presence of other activities, perhaps providing a step forward for at-home monitoring.

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“…The distinction between hand and joint movement warrants attention, not least for reasons of practicality. Hand movement may be recorded with a single sensor unit, or even with standard smartphones or smartwatches [19], making it particularly well suited to frequent at-home monitoring. This convenience could conceivably allow closer clinical monitoring without substantially increasing the burden on the patient, and our approach could easily be incorporated into those systems.…”

Section: Discussionmentioning

confidence: 99%

Personalised profiling to identify clinically relevant changes in tremor due to multiple sclerosis

Western

Neild

Jones

et al. 2019

BMC Med Inform Decis Mak

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Background There is growing interest in sensor-based assessment of upper limb tremor in multiple sclerosis and other movement disorders. However, previously such assessments have not been found to offer any improvement over conventional clinical observation in identifying clinically relevant changes in an individual’s tremor symptoms, due to poor test-retest repeatability. Method We hypothesised that this barrier could be overcome by constructing a tremor change metric that is customised to each individual’s tremor characteristics, such that random variability can be distinguished from clinically relevant changes in symptoms. In a cohort of 24 people with tremor due to multiple sclerosis, the newly proposed metrics were compared against conventional clinical and sensor-based metrics. Each metric was evaluated based on Spearman rank correlation with two reference metrics extracted from the Fahn-Tolosa-Marin Tremor Rating Scale: a task-based measure of functional disability (FTMTRS B) and the subject’s self-assessment of the impact of tremor on their activities of daily living (FTMTRS C). Results Unlike the conventional sensor-based and clinical metrics, the newly proposed ’change in scale’ metrics presented statistically significant correlations with changes in self-assessed impact of tremor ( max R 2 >0.5, p <0.05 after correction for false discovery rate control). They also outperformed all other metrics in terms of correlations with changes in task-based functional performance ( R 2 =0.25 vs. R 2 =0.15 for conventional clinical observation, both p <0.05). Conclusions The proposed metrics achieve an elusive goal of sensor-based tremor assessment: improving on conventional visual observation in terms of sensitivity to change. Further refinement and evaluation of the proposed techniques is required, but our core findings imply that the main barrier to translational impact for this application can be overcome. Sensor-based tremor assessments may improve personalised treatment selection and the efficiency of clinical trials for new treatments by enabling greater standardisation and sensitivity to clinically relevant changes in symptoms.

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

Cited by 32 publications

References 32 publications

The Role of Wearable Devices in Multiple Sclerosis

The Role of Wearable Devices in Multiple Sclerosis

Quantifying Tremor in Essential Tremor Using Inertial Sensors—Validation of an Algorithm

Personalised profiling to identify clinically relevant changes in tremor due to multiple sclerosis

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