Neurodegenerative Disease Monitoring Using a Portable Wireless Sensor Device

Bustamante, P.; Solas, Gonzalo; Grandez, K.

doi:10.5772/29814

Cited by 4 publications

(2 citation statements)

References 11 publications

(11 reference statements)

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“…Analyzing gait patterns in free living conditions is an important component in the detection and diagnosis of a variety of medical conditions such as injury detection [1], obesity [2], and Parkinson's disease [3]. Gait analysis has also been used as a tool to gauge the effectiveness of mobility-related physical therapy treatments outside the clinic to maximize the rate at which a patient's gait recovers to normal [1].…”

Section: Introductionmentioning

confidence: 99%

A study of position independent algorithms for phone-based gait frequency detection

Tarashansky¹,

Vathsangam

Sukhatme

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Estimating gait frequency is an important component in the detection and diagnosis of various medical conditions. Smartphone-based kinematic sensors offer a window of opportunity in free-living gait frequency estimation. The main issue with smartphone-based gait frequency estimation algorithms is how to adjust for variations in orientation and location of the phone on the human body. While numerous algorithms have been implemented to account for these differences, little work has been done in comparing these algorithms. In this study, we compare various position independent algorithms to determine which are more suited to robust gait frequency estimation. Using sensor data collected from volunteers walking with a smartphone, we examine the effect of using three different time series with the magnitude, weighted sum, and closest vertical component algorithms described in the paper. We also test two different methods of extracting step frequency: time domain peak counting and spectral analysis. The results show that the choice of time series does not significantly affect the accuracy of frequency measurements. Furthermore, both time domain and spectral approaches show comparable results. However, time domain approaches are sensitive to false-positives while spectral approaches require a minimum set of repetitive measurements. Our study suggests a hybrid approach where both time-domain and spectral approaches be used together to complement each other's shortcomings.

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Section: Introductionmentioning

confidence: 99%

A study of position independent algorithms for phone-based gait frequency detection

Tarashansky¹,

Vathsangam

Sukhatme

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…A bradykinesia profiler was also presented on that work. [360], [485]- [488]. Additionally, intelligent algorithms were built on top to work as DSS.…”

Section: Project Descriptionmentioning

confidence: 99%

A holistic approach to assess the feasability of telehealth for Parkinson's disease management

González¹

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Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

et al. 2023

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Objective Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases. Methods A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection. Results The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance. Conclusions This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.

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Neurodegenerative Disease Monitoring Using a Portable Wireless Sensor Device

Cited by 4 publications

References 11 publications

A study of position independent algorithms for phone-based gait frequency detection

A study of position independent algorithms for phone-based gait frequency detection

A holistic approach to assess the feasability of telehealth for Parkinson's disease management

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

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