Freezing-of-Gait Detection Using Wearable Sensor Technology and Possibilistic K-Nearest-Neighbor Algorithm

Tahafchi, Parisa; Judy, Jack W.

doi:10.1109/embc.2019.8856480

Cited by 9 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jack W. Judy et., al. [7], proposed the Parkinson's disease can affect the patient in the major part. It affects the locomotion of the patient.…”

Section: Literature Surveymentioning

confidence: 99%

Machine Learning Algorithms for Detection of Parkinson’s Disease using Motor Symptoms: Speech and Tremor

Bala¹,

Anusuya²

2020

IJRTE

View full text Add to dashboard Cite

Generally, the diseases are classified into communicable and non-communicable. The communicable disease is that, which can be spread easily from humans to humans while non-communicable disease does not spread. In this paper, we discuss about Parkinson's disease and its analysis using machine learning algorithms. The analysis of data is done using supervised machine learning approach. This paper concentrates and briefs about various supervised learning algorithms and its analysis.

show abstract

“…Jack W. Judy et., al. [7], proposed the Parkinson's disease can affect the patient in the major part. It affects the locomotion of the patient.…”

Section: Literature Surveymentioning

confidence: 99%

Machine Learning Algorithms for Detection of Parkinson’s Disease using Motor Symptoms: Speech and Tremor

Bala¹,

Anusuya²

2020

IJRTE

View full text Add to dashboard Cite

show abstract

“…Many classification approaches have been used to classify tremors versus normal states, mainly for Parkinson disease or essential tremor disorder [57][58][59][60][61][62][63][64]. However, these approaches have not been applied to tremors caused by hypoglycemia.…”

Section: Classification Modelsmentioning

confidence: 99%

Hypoglycemia Detection Using Hand Tremors: Home Study of Patients With Type 1 Diabetes

Jahromi¹,

Zahed²,

Erraguntla³

et al. 2023

JMIR Diabetes

View full text Add to dashboard Cite

Background Diabetes affects millions of people worldwide and is steadily increasing. A serious condition associated with diabetes is low glucose levels (hypoglycemia). Monitoring blood glucose is usually performed by invasive methods or intrusive devices, and these devices are currently not available to all patients with diabetes. Hand tremor is a significant symptom of hypoglycemia, as nerves and muscles are powered by blood sugar. However, to our knowledge, no validated tools or algorithms exist to monitor and detect hypoglycemic events via hand tremors. Objective In this paper, we propose a noninvasive method to detect hypoglycemic events based on hand tremors using accelerometer data. Methods We analyzed triaxial accelerometer data from a smart watch recorded from 33 patients with type 1 diabetes for 1 month. Time and frequency domain features were extracted from acceleration signals to explore different machine learning models to classify and differentiate between hypoglycemic and nonhypoglycemic states. Results The mean duration of the hypoglycemic state was 27.31 (SD 5.15) minutes per day for each patient. On average, patients had 1.06 (SD 0.77) hypoglycemic events per day. The ensemble learning model based on random forest, support vector machines, and k-nearest neighbors had the best performance, with a precision of 81.5% and a recall of 78.6%. The results were validated using continuous glucose monitor readings as ground truth. Conclusions Our results indicate that the proposed approach can be a potential tool to detect hypoglycemia and can serve as a proactive, nonintrusive alert mechanism for hypoglycemic events.

show abstract

“…Usually, WBSNs-based gait pattern can be constructed by different sensors node that are located on the different anatomical regions of body such as head, shoulder, elbow, arm, wrist, waist, hip, thigh, knee, ankle, heel, foot and so on. Many studies have found that automatic recognition of WBSNs gait pattern has greatly contributes to accurately evaluate the human gait function change in daily life [6][7][8][9], which benefits clinical diagnosis, rehabilitation assessment and early prediction of fall risk of elderly, and so on. In recent studies, automatic recognition of WBSNs gait pattern has been considered as a gait classification task, and the challenging issue is how to achieve the best generalization performance and potential interpretation for gait variability [8][9].…”

Section: Introductionmentioning

confidence: 99%

An Advanced Attention-Enhanced Hybrid Deep Learning Model for WBSNs’ Gait Pattern Recognition

Tan

2022

Preprint

View full text Add to dashboard Cite

Background: The deep learning techniques have been attracted increasing attention on wireless body sensor networks (WBSNs) gait pattern recognition that has a great contribution to monitoring gait change in clinical application. However, in existing studies, there are some challenging issues such as low generalization performance and no potential interpretation for gait variability. It is necessary to search for the advanced deep learning models to resolve these issues. Method: A public WARD database including acceleration and gyroscope data acquired from each subject wearing five sensors was selected, and the gait with different combination of on-body multi-sensors is considered as a WBSNs’ gait pattern. An advanced attention-enhanced hybrid deep learning model of DCNN and LSTM for WBSNs’ gait pattern recognition was proposed. In our proposed technique, the combination model of DCNN with LSTM is firstly to discover the spatial-temporary gait correlation features. And then the attention mechanism is introduced to exploit the more valuable intrinsic nonlinear dynamic correlation gait characteristics associated with gait variability hidden in spatial-temporary gait space obtained. This significantly contributes to enhancing the generalization performance and taking insight on gait variability in a certain anatomical region. Results: The ten gait patterns are randomly selected from WARD database to evaluate the feasibility of our proposed method. Our experiments demonstrated the superior generalization ability of our method to some models such as CNN-LSTM, DCNN-LSTM. Our proposed model could classify ten gait patterns with the highest accuracy and F1-score of 91.48% and 91.46%, respectively. Moreover, we also found that the classification performance of a certain gait pattern was almost same best when the combinations of three or five on-body sensors were employed respectively, suggesting that our method possibly take insight on gait variability in a certain anatomical region. Conclusion: Our proposed technique could feasibly discover the more intrinsic nonlinear dynamic correlation gait characteristics associated with gait variability from on-body multi-sensors gait data, which greatly contributed to best generalization performance and potential clinical interpretation. Our proposed technique would hopefully become a powerful tool of monitoring gait change in clinical application.

show abstract

Freezing-of-Gait Detection Using Wearable Sensor Technology and Possibilistic K-Nearest-Neighbor Algorithm

Cited by 9 publications

References 13 publications

Machine Learning Algorithms for Detection of Parkinson’s Disease using Motor Symptoms: Speech and Tremor

Machine Learning Algorithms for Detection of Parkinson’s Disease using Motor Symptoms: Speech and Tremor

Hypoglycemia Detection Using Hand Tremors: Home Study of Patients With Type 1 Diabetes

An Advanced Attention-Enhanced Hybrid Deep Learning Model for WBSNs’ Gait Pattern Recognition

Contact Info

Product

Resources

About