A smartphone-based system for detecting hand tremors in unconstrained environments

García‐Magariño, Iván; Medrano, Carlos; Plaza, Inmaculada; Oliván, Bárbara

doi:10.1007/s00779-016-0956-2

Cited by 24 publications

(33 citation statements)

References 19 publications

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“…A large number of feature sets have been proposed for PD detection. The vast majority rely on time domain features (such as the mean, range, or cross-correlation) [ 13 , 14 ], frequency domain features (such as the dominant frequency, energy content in a particular band, or signal entropy) [ 15 , 16 ], or a combination of the two [ 17 , 18 , 19 , 20 ]. Some authors have shown that features that are traditionally used for speech processing (e.g., Mel frequency, Cepstral coefficients) are also effective for classifying human motion from accelerometer data [ 21 , 22 ].…”

Section: Related Workmentioning

confidence: 99%

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Parkinson’s Disease Tremor Detection in the Wild Using Wearable Accelerometers

San-Segundo

Zhang

Cebulla

et al. 2020

Sensors

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Continuous in-home monitoring of Parkinson’s Disease (PD) symptoms might allow improvements in assessment of disease progression and treatment effects. As a first step towards this goal, we evaluate the feasibility of a wrist-worn wearable accelerometer system to detect PD tremor in the wild (uncontrolled scenarios). We evaluate the performance of several feature sets and classification algorithms for robust PD tremor detection in laboratory and wild settings. We report results for both laboratory data with accurate labels and wild data with weak labels. The best performance was obtained using a combination of a pre-processing module to extract information from the tremor spectrum (based on non-negative factorization) and a deep neural network for learning relevant features and detecting tremor segments. We show how the proposed method is able to predict patient self-report measures, and we propose a new metric for monitoring PD tremor (i.e., percentage of tremor over long periods of time), which may be easier to estimate the start and end time points of each tremor event while still providing clinically useful information.

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Section: Related Workmentioning

confidence: 99%

“…Researchers have experimented with a wide variety of standard ML algorithms, such as decision trees [ 14 ], support vector machines (SVMs) [ 17 ], random forests (RFs) [ 23 ], hidden Markov models [ 15 ], and dynamic neural networks [ 13 ]. Some studies compared several of these algorithms [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

Parkinson’s Disease Tremor Detection in the Wild Using Wearable Accelerometers

San-Segundo

Zhang

Cebulla

et al. 2020

Sensors

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“…The human walking speed V h is 0.75-2 m/s (Donelan et al 2002). On the other hand, the acceleration due to camera García-Magariño et al 2016). Here, V c2 and V c1 are the instantaneous movement velocity of the smartphone at time t 2 and time t 1 .…”

Section: Appendix Error Estimation Of Image Registrationmentioning

confidence: 99%

Mobile visualization of density fields using smartphone background-oriented schlieren

Hayasaka

Tagawa

2019

Exp Fluids

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The conventional background-oriented schlieren (BOS) technique is an image-based technique that can calculate the density field in fluids using two static images [i.e., an undistorted background image (reference image) and a distorted background image due to the density change in fluids (target image)]. This paper proposes the smartphone BOS (SBOS) technique, which offers the measurement of the density gradient using the high-speed imaging feature of the smartphone being carried with a moving observer. The conventional BOS with a fixed camera visualizes the density gradient by comparing the reference image and the target image. In contrast, SBOS can obtain the time difference of the density gradient field. A reference image in SBOS is a target one at a previous time step. The movement of the smartphone is canceled using a registration technique for image accurate alignment. Three demonstrations are conducted to perform SBOS. First, in a static situation, the density field of heated air by a gas burner is visualized by comparing between SBOS and conventional BOS. Second, the local displacement of density field and the error displacement is estimated quantitatively when the smartphone is moving. Third, SBOS using an embossed wallpaper to visualize the density field is performed in the mobile condition. These achievements suggest that SBOS is an effective system to visualize the density field using only the smartphone, and is expected to be a useful tool such as a preliminary experiment in the laboratory and a teaching tool for general smartphone users.

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“…Data collected outside the lab are often labeled to provide further context in data analysis, including manual hand labeling by a trained expert, user self-annotations [ 26 ] and video monitoring of users. Video recordings are subsequently manually annotated and are common in continuous patient monitoring systems including tremor detection [ 27 ] or PD disease severity assessment [ 28 ]. Using video annotations significantly complicates the experimental setup.…”

Section: Introductionmentioning

confidence: 99%

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

Badawy

Raykov

Evers

et al. 2018

Sensors

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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.

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A smartphone-based system for detecting hand tremors in unconstrained environments

Cited by 24 publications

References 19 publications

Parkinson’s Disease Tremor Detection in the Wild Using Wearable Accelerometers

Parkinson’s Disease Tremor Detection in the Wild Using Wearable Accelerometers

Mobile visualization of density fields using smartphone background-oriented schlieren

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

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