Meta-Classifiers in Huntington’s Disease Patients Classification, Using iPhone’s Movement Sensors Placed at the Ankles

Acosta, Francisco; Beltrán-Naturi, Elías; Boll, Marie Catherine; Hernandez-Nolasco, Jose Adan; Pancardo, Pablo

doi:10.1109/access.2018.2840327

Cited by 17 publications

(9 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Other studies used wearable sensors to monitor sleep-wake activity (time spent asleep and motor activity during sleep) [ 45 ], as well as sleep measurements (total sleep time, sleep latency, sleep efficiency, and wake after sleep onset) [ 39 ], or circadian rhythm [ 40 , 41 ]. Several studies have investigated balance [ 38 ] and walking/gait characteristics [ 25 , 26 , 28 , 30 , 32 , 33 , 37 , 38 , 42 , 44 , 48 , 52 ]. Kegelmeyer et al made a quantitative biomechanical assessment of trunk control, measuring the trunk stability during standing, sitting and walking, and the ability of individuals to modify trunk position responding to some auditory cues [ 50 ].…”

Section: Resultsmentioning

confidence: 99%

“…Extracting meaningful and useful outcomes from high-dimension datasets is a major challenge as digital biomarker technology becomes ever more complex. That was the reason why some of the studies focused on advanced machine learning approaches and new algorithms or analysis methods to extract parameters with the best discrimination ability and increase the classification accuracy between HD and controls [25,32,37,44,51,52]. However, none of the proposed algorithms has been reproduced in a replication cohort.…”

Section: Performance Of Wearable Devices In Hd: What Did They Add To mentioning

confidence: 99%

See 1 more Smart Citation

The use of wearable/portable digital sensors in Huntington's disease: A systematic review

Tortelli

Rodrigues

Wild

2021

Parkinsonism & Related Disorders

View full text Add to dashboard Cite

In chronic neurological conditions, wearable/portable devices have potential as innovative tools to detect subtle early disease manifestations and disease fluctuations for the purpose of clinical diagnosis, care and therapeutic development. Huntington's disease (HD) has a unique combination of motor and non-motor features which, combined with recent and anticipated therapeutic progress, gives great potential for such devices to prove useful. The present work aims to provide a comprehensive account of the use of wearable/portable devices in HD and of what they have contributed so far. We conducted a systematic review searching MEDLINE, Embase, and IEEE Xplore. Thirty references were identified. Our results revealed large variability in the types of sensors used, study design, and the measured outcomes. Digital technologies show considerable promise for therapeutic research and clinical management of HD. However, more studies with standardized devices and harmonized protocols are needed to optimize the potential applicability of wearable/portable devices in HD.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Performance Of Wearable Devices In Hd: What Did They Add To mentioning

confidence: 99%

The use of wearable/portable digital sensors in Huntington's disease: A systematic review

Tortelli

Rodrigues

Wild

2021

Parkinsonism & Related Disorders

View full text Add to dashboard Cite

show abstract

“…This method is based on a supervised and trained two-state hidden Markov model, which can be extended to different research subjects for clinical practice and personal health assessment. At the same time, Francisco et al collected the gait data of HD patients by binding the iPhone on the ankles of patients, using the built-in smart sensor of iPhone, and classified the data with the general assembly (meta) classifier algorithm to distinguish normal people and HD patients [14]. The use of wearable devices to monitor the movements and gaits of HD patients requires the binding of electronic devices to a part of the patient's body, which will affect the patient's movements to a certain extent and cannot collect movement data in the natural state of the patient.…”

Section: Related Workmentioning

confidence: 99%

Monitoring of Huntington’s Disease Based on Wireless Sensing Technology

et al. 2020

View full text Add to dashboard Cite

Huntington’s disease (HD) is a rare genetic disorder that cannot be cured by current medical techniques. With the development of the disease, the life of patients will become more and more difficult. It is necessary to timely and effectively evaluate the development of the patient’s condition based on the patient’s clinical symptoms to help doctors to formulate a reasonable and effective treatment plan, alleviate the condition, and improve the quality of life, which reflects humane care. Currently, wearable devices or video surveillance are generally used to monitor the patients, and these schemes have some disadvantages. This paper presents a new method to monitor patients with HD using wireless sensing technology. Firstly, experimental data were collected by the self-developed microwave sensing platform (MSP), and then the data were preprocessed. Finally, support vector machine (SVM) and random forest (RF) algorithms were used to train the model. The MSP system continuously monitors patients in a non-contact way, which will not bring inconvenience to patients’ lives, and will not involve privacy issues. The experimental results show that the prediction accuracy of SVM can be as high as 98.0% and that of RF can be as high as 96.7%, which proves the feasibility of the technical scheme described in this paper.

show abstract

“…Gait features based on spatio-temporal parameters of frequency domain used in this work, were introduced in an exploratory study to analyze gait patterns of subjects with Complex Regional Pain Syndrome (CRPS), using data from a DynaPort MiniMod triaxial accelerometer [33]. These gait features were used in a previous study to improve the results in the classification of people with Huntington's disease, in which the results obtained with these characteristics were also validated against those obtained from a stratified sensors dataset [26].…”

Section: Related Workmentioning

confidence: 99%

“…iPhone smartphone sensors have been shown to be accurate and reliable enough to evaluate and identify kinematic gait patterns [21,22], the iPhone's ability to capture gait characteristics with a sufficient level of consistency in ankle position has been shown in studies related to gait assessment and health care, as well as its comfort, portability and wearability [23][24][25]. In previous works, gait data collected with movement sensors from patients with Huntington's disease and healthy elderly people were compared and validated by implementing automatic learning techniques with several meta-classification algorithms [26].…”

Section: Introductionmentioning

confidence: 99%

Gait Pattern Recognition in Patients with Hereditary Ataxia’s Using Supervised Learning Algorithms Based on Small Subsets Smartphone Sensor Data<b></b>

Acosta¹,

Beltrán-Naturi²,

Boll³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

The progressive impairment analysis in gait from neurological diseases patients such as Hereditary Ataxias (HA) has been carried out using gait data collected with movement sensors. This research is focused on finding the minimum amount required of gait features to recognize efficiently and less intrusive way, HA patients based on data collected with iPhone movement sensors placed on the ankles from 14 HA patients and 14 healthy people. A twofold proposal is made , first a local minimum prominent peak criterion to find out the starting point of each stride, to get 10-stride window about which 56 spatial-temporal features are derived; second a search strategy based on Hill Climbing algorithm to reduce the number of gait features and sensors. The main results were the findings that with two gait patterns a 96% of classification accuracy was achieved by using K-Nearest Neighbors (KNN) and Multi-Layer Perceptron (MLP) algorithms, but in addition, MLP only right ankle sensor patterns were required which also allows to reduce the intrusion.

show abstract

Meta-Classifiers in Huntington’s Disease Patients Classification, Using iPhone’s Movement Sensors Placed at the Ankles

Cited by 17 publications

References 46 publications

The use of wearable/portable digital sensors in Huntington's disease: A systematic review

The use of wearable/portable digital sensors in Huntington's disease: A systematic review

Monitoring of Huntington’s Disease Based on Wireless Sensing Technology

Gait Pattern Recognition in Patients with Hereditary Ataxia’s Using Supervised Learning Algorithms Based on Small Subsets Smartphone Sensor Data<b></b>

Contact Info

Product

Resources

About