Weakly-supervised learning for Parkinson's Disease tremor detection

Zhang, Ada; Cebulla, Alexander; Panev, Stanislav; Hodgins, Jessica K.; Torre, Fernando De la

doi:10.1109/embc.2017.8036782

Cited by 19 publications

(35 citation statements)

References 13 publications

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“…The more recent work of [24], extended the previous approach by using a dataset of 5 PD patients all of which exhibited tremor. Data collection took place under laboratory conditions that resembled a home environment.…”

Section: Related Workmentioning

confidence: 99%

Detecting Parkinsonian Tremor From IMU Data Collected in-the-Wild Using Deep Multiple-Instance Learning

Παπαδόπουλος

Kyritsis

Klingelhoefer

et al. 2020

IEEE J. Biomed. Health Inform.

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Parkinson's Disease (PD) is a slowly evolving neurological disease that affects about 1% of the population above 60 years old, causing symptoms that are subtle at first, but whose intensity increases as the disease progresses. Automated detection of these symptoms could offer clues as to the early onset of the disease, thus improving the expected clinical outcomes of the patients via appropriately targeted interventions. This potential has led many researchers to develop methods that use widely available sensors to measure and quantify the presence of PD symptoms such as tremor, rigidity and braykinesia. However, most of these approaches operate under controlled settings, such as in lab or at home, thus limiting their applicability under free-living conditions. In this work, we present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device. We propose a Multiple-Instance Learning approach, wherein a subject is represented as an unordered bag of accelerometer signal segments and a single, expert-provided, tremor annotation. Our method combines deep feature learning with a learnable pooling stage that is able to identify key instances within the subject bag, while still being trainable end-to-end. We validate our algorithm on a newly introduced dataset of 45 subjects, containing accelerometer signals collected entirely in-the-wild. The good classification performance obtained in the conducted experiments suggests that the proposed method can efficiently navigate the noisy environment of in-the-wild recordings.

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Section: Related Workmentioning

confidence: 99%

Detecting Parkinsonian Tremor From IMU Data Collected in-the-Wild Using Deep Multiple-Instance Learning

Παπαδόπουλος

Kyritsis

Klingelhoefer

et al. 2020

IEEE J. Biomed. Health Inform.

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“…At best, weak labels (presence, absence, or the approximate amount of a symptom within a time segment) can be provided by participants of the data collections. Weakly supervised algorithms, such as multiple-instance learning, explicitly account for weak labels, and tend to perform better than standard, fully supervised learning algorithms in these scenarios [ 4 ]. In summary, collecting data, labeling them, and learning from such data are all more challenging in wild environments.…”

Section: Introductionmentioning

confidence: 99%

“…Each feature set is followed with either a Random Forest (RF) or Multi-Layer Perceptron (MLP) classifier to distinguish the various performance contributions of features versus algorithms. We compare the performance of our best feature set and algorithm combination with the three most relevant systems described in the literature: [ 4 , 6 , 7 ]. We also compare our method with [ 4 ] (best among [ 6 , 7 ]) in the ability to reproduce patient self-reports, the current standard for in-home symptom monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…We compare the performance of our best feature set and algorithm combination with the three most relevant systems described in the literature: [ 4 , 6 , 7 ]. We also compare our method with [ 4 ] (best among [ 6 , 7 ]) in the ability to reproduce patient self-reports, the current standard for in-home symptom monitoring. Finally, we propose a new metric for monitoring PD tremor (percentage of tremor time measured over longer periods), which is easier to detect than specific tremor events.…”

Section: Introductionmentioning

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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“…Radar based approaches used predefined features from the time-frequency representation (TFR) of the radar return signal to monitor human gait [2]. Sensor based approaches detect gait abnormality or tremors using contact sensors such as MetaMotionRan Inertial Measurement Unit (IMU) sensor from Mbientlab [3] and the Axivity AX3 accelerometer [4]. Camera based approaches recognizes gait abnormality or tremors using the Kinect camera [5], [6].…”

mentioning

confidence: 99%

$S$ -Band Sensing-Based Motion Assessment Framework for Cerebellar Dysfunction Patients

et al. 2019

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Cerebellar dysfunction (CD) is a neurological disorder that involves a number of abnormalities that affect the movement of various parts of the body such as gait abnormality or tremors in limbs such as hands or feet while reaching out for something. A user-friend tool that can objectively evaluate the aforementioned body movements in CD patients can aid the clinicians for an objective assessment in clinical settings. The objective of this work is to develop a method that quantifies the gait abnormality and tremors in hand using S-Band sensing technique. The S-Band sensing essentially leverages small wireless devices such as network interface card, omnidirectional antenna and router operating at 2.4 GHz to record the wireless channel data. Specifically, the aim is to use the variances of amplitude and phase information induced due to the human body movements. Each body movement leaves a unique imprint in the form of wireless channel information that is used to identify abnormalities in body motions. The proposed framework applied a linear transformation on raw phase data for calibrations since the data retrieved using interface card contain noise and is inapplicable for motion detection. The support vector machine used to classify the data achieved high classification accuracy.

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Weakly-supervised learning for Parkinson's Disease tremor detection

Cited by 19 publications

References 13 publications

Detecting Parkinsonian Tremor From IMU Data Collected in-the-Wild Using Deep Multiple-Instance Learning

Detecting Parkinsonian Tremor From IMU Data Collected in-the-Wild Using Deep Multiple-Instance Learning

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

$S$ -Band Sensing-Based Motion Assessment Framework for Cerebellar Dysfunction Patients

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