Smartphone-based automatic measurement of the results of the Timed-Up and Go test

Ponciano, Vasco; Pires, Ivan Miguel; Ribeiro, Fernando; García, Nuno M.; Spinsante, Susanna; Crisóstomo, Rute

doi:10.1145/3342428.3343035

Cited by 14 publications

(12 citation statements)

References 18 publications

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“…As future work, we intend to identify different diseases based on the performance of the Timed-Up and Go test as the continuation of the work presented at [75,76]. The recognized disorders will be mainly related to the different abnormalities of movement, and other healthcare problems related to the lower limbs.…”

Section: Discussionmentioning

confidence: 99%

Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

et al. 2020

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Inertial sensors are commonly embedded in several devices, including smartphones, and other specific devices. This type of sensors may be used for different purposes, including the recognition of different diseases. Several studies are focused on the use of accelerometer signals for the automatic recognition of different diseases, and it may empower the different treatments with the use of less invasive and painful techniques for patients. This paper aims to provide a systematic review of the studies available in the literature for the automatic recognition of different diseases by exploiting accelerometer sensors. The most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson’s disease. The machine learning methods implemented for the automatic recognition of Parkinson’s disease reported an accuracy of 94%. The recognition of other diseases is investigated in a few other papers, and it appears to be the target of further analysis in the future.

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Section: Discussionmentioning

confidence: 99%

Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

et al. 2020

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“…Following the previous studies [23,24], the data, as presented in Table 1, were acquired from 14 institutionalized individuals aged between 71 and 97 years old (83 +/− 7.4) with different diseases included in some categories, as presented in Section 3.4. The different data were acquired from various institutions, such as Centro Comunitário das Lameiras, Lar Minas, Lar da Misericórdia, and Lar da Nossa Senhora de Fátima.…”

Section: Data Collectionmentioning

confidence: 99%

“…The main purpose of this study was the implementation of neural networks to identify the different diseases present in the population considered in the study reported in [23,24]. It was related to the timed-up and go test's execution with institutionalized people from the Covilhã and Fundão municipalities.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques with ECG and EEG Data: An Exploratory Study

et al. 2020

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Electrocardiography (ECG) and electroencephalography (EEG) are powerful tools in medicine for the analysis of various diseases. The emergence of affordable ECG and EEG sensors and ubiquitous mobile devices provides an opportunity to make such analysis accessible to everyone. In this paper, we propose the implementation of a neural network-based method for the automatic identification of the relationship between the previously known conditions of older adults and the different features calculated from the various signals. The data were collected using a smartphone and low-cost ECG and EEG sensors during the performance of the timed-up and go test. Different patterns related to the features extracted, such as heart rate, heart rate variability, average QRS amplitude, average R-R interval, and average R-S interval from ECG data, and the frequency and variability from the EEG data were identified. A combination of these parameters allowed us to identify the presence of certain diseases accurately. The analysis revealed that the different institutions and ages were mainly identified. Still, the various diseases and groups of diseases were difficult to recognize, because the frequency of the different diseases was rare in the considered population. Therefore, the test should be performed with more people to achieve better results.

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“…The primary motivation of this work consists in the continuation of the previous studies [36,37] with the acquisition of data from the accelerometer and magnetometer sensors available on the offthe-shelf mobile devices, pressure sensors available in the back of the chair, and ECG and EEG sensors, for the detection of different types of movement, and cardiac and brain problems. Based on the healthcare problems of the population analyzed, we intend to design and develop a method for the acquisition, analysis, and identification of different patterns of diseases with low-cost sensors.…”

Section: Motivationmentioning

confidence: 99%

Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases During the Timed-Up and Go Test

Ponciano¹,

Pires²,

Ribeiro³

et al. 2020

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The use of smartphones, coupled with different sensors, makes it an attractive solution for measuring different physical and physiological features, allowing for the monitoring of various parameters and even identifying some diseases. The BITalino device allows the use of different sensors, including Electroencephalography (EEG) and Electrocardiography (ECG) sensors, to study different health parameters. With these devices, the acquisition of signals is straightforward, and it is possible to connect them using a Bluetooth connection. With the acquired data, it is possible to measure parameters such as calculating the QRS complex and its variation with ECG data to control the individual's heartbeat. Similarly, by using the EEG sensor one could analyze the individual's brain activity and frequency. The purpose of this paper is to present a method for recognition of the diseases related to ECG and EEG data, with sensors available in off-the-shelf mobile devices and sensors connected to a BITalino device. The data were collected during the elderly's experiences, performing the Timed-Up and Go test, and the different diseases found in the sample in the study. The data were analyzed, and the following features were extracted from the ECG, including heart rate, heart rate variability, the average QRS interval, the average R-R interval, and the average R-S interval, and the EEG, including frequency and variability. Finally, the diseases are correlated with different parameters, proving that there are relations between the individuals and the different health conditions.

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Smartphone-based automatic measurement of the results of the Timed-Up and Go test

Cited by 14 publications

References 18 publications

Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

Machine Learning Techniques with ECG and EEG Data: An Exploratory Study

Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases During the Timed-Up and Go Test

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