Measuring tremor with a smartphone

Assessing the Usability of an Instagram Filter in Monitoring Essential Tremor: A Proof-of-Concept Study

Background

Spiral drawing is an important test in monitoring essential tremor (ET). With the rise of telemedicine amid the coronavirus disease 2019 pandemic, a contactless tool for monitoring tremors was required. We aimed to assess the validity of a novel smartphone technology using a video-based social media platform for rapid and objective monitoring of ET.

Methods

A prospective pilot study evaluated patients with ET in 2 clinic visits. Videos of tremors were recorded using a publicly available Instagram filter and were visually compared with spirals drawn by the patients. The level of agreement among the raters was evaluated.

Results

A total of 12 patients with ET were recruited. A consensus between both raters was achieved for 11 patients (91.6%) for both spirals and videos with good interrater agreement (κ value, 0.755 ± 0.332).

Conclusion

This novel method was found to be valid and easy to use in measuring ET in real-world settings. Further research in a larger cohort is needed to suggest its use as a home-based or clinic-based monitoring tool.

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.

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.

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.

A Validation Study of a Smartphone-Based Finger Tapping Application for Quantitative Assessment of Bradykinesia in Parkinson's Disease

Background

Most studies of smartphone-based assessments of motor symptoms in Parkinson’s disease (PD) focused on gait, tremor or speech. Studies evaluating bradykinesia using wearable sensors are limited by a small cohort size and study design. We developed an application named smartphone tapper (SmT) to determine its applicability for clinical purposes and compared SmT parameters to current standard methods in a larger cohort.

Methods

A total of 57 PD patients and 87 controls examined with motor UPDRS underwent timed tapping tests (TT) using SmT and mechanical tappers (MeT) according to CAPSIT-PD. Subjects were asked to alternately tap each side of two rectangles with an index finger at maximum speed for ten seconds. Kinematic measurements were compared between the two groups.

Results

The mean number of correct tapping (MCoT), mean total distance of finger movement (T-Dist), mean inter-tap distance, and mean inter-tap dwelling time (IT-DwT) were significantly different between PD patients and controls. MCoT, as assessed using SmT, significantly correlated with motor UPDRS scores, bradykinesia subscores and MCoT using MeT. Multivariate analysis using the SmT parameters, such as T-Dist or IT-DwT, as predictive variables and age and gender as covariates demonstrated that PD patients were discriminated from controls. ROC curve analysis of a regression model demonstrated that the AUC for T-Dist was 0.92 (95% CI 0.88–0.96).

Conclusion

Our results suggest that a smartphone tapping application is comparable to conventional methods for the assessment of motor dysfunction in PD and may be useful in clinical practice.