Calculation of Jump Flight Time using a Mobile Device

Pires, Ivan Miguel; García, Nuno M.; Teixeira, Maria Cristina Canavarro

doi:10.5220/0005187502930303

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…This project started with the definition of two different sets: ADLs that may be reliably identified with mobile devices, and, valid sensors available in mobile devices. This work continues the research presented in [1], which included the calculation of a jump flight time that makes the use of pattern recognition techniques to identify patterns in vertical jumps [2]. Another work where similar analysis has been carried out is related to the Heel-Rise test [3], based on a test used mainly by physiotherapists that allows the detection of fatigue and/or specific diseases.…”

Section: Introductionmentioning

confidence: 89%

Identification of Activities of Daily Living Using Sensors Available in off-the-shelf Mobile Devices: Research and Hypothesis

Pires

García

Flórez-Revuelta³

2016

Ambient Intelligence- Software and Applications – 7th International Symposium on Ambient Intelligence (ISAmI 2016)

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This paper presents a PhD project related to the identification of a set of Activities of Daily Living (ADLs) using different techniques applied to the sensors available in off-the-shelf mobile devices. This project consists on the creation of new methodologies, to identify ADLs, and to present some concepts, such as definition of the set of ADLs relevant to be identified, the mobile device as a multi-sensor system, review of the best techniques for data acquisition, data processing, data validation, data imputation, and data fusion processes, and creation of the methods for the identification of ADLs with data mining, pattern recognition and/or machine learning techniques. However, mobile devices present several limitations, therefore techniques at each stage have to be adapted. As result I.M. Pires() · N.M. Garcia · N. Pombo

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Section: Introductionmentioning

confidence: 89%

Identification of Activities of Daily Living Using Sensors Available in off-the-shelf Mobile Devices: Research and Hypothesis

Pires

García

Flórez-Revuelta³

2016

Ambient Intelligence- Software and Applications – 7th International Symposium on Ambient Intelligence (ISAmI 2016)

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“…As part of the research on developing solutions for Ambient Assisted Living (AAL) [ 44 , 45 , 46 ], the scope of this study consists of using technological equipment that embeds inertial sensors that acquire different data types to measure and identify human movements [ 47 , 48 , 49 , 50 ].…”

Section: Methodsmentioning

confidence: 99%

An Experimental Study on the Validity and Reliability of a Smartphone Application to Acquire Temporal Variables during the Single Sit-to-Stand Test with Older Adults

Marques

Neiva

Pires

et al. 2021

Sensors

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Smartphone sensors have often been proposed as pervasive measurement systems to assess mobility in older adults due to their ease of use and low-cost. This study analyzes a smartphone-based application’s validity and reliability to quantify temporal variables during the single sit-to-stand test with institutionalized older adults. Forty older adults (20 women and 20 men; 78.9 ± 8.6 years) volunteered to participate in this study. All participants performed the single sit-to-stand test. Each sit-to-stand repetition was performed after an acoustic signal was emitted by the smartphone app. All data were acquired simultaneously with a smartphone and a digital video camera. The measured temporal variables were stand-up time and total time. The relative reliability and systematic bias inter-device were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. In contrast, absolute reliability was assessed using the standard error of measurement and coefficient of variation (CV). Inter-device concurrent validity was assessed through correlation analysis. The absolute percent error (APE) and the accuracy were also calculated. The results showed excellent reliability (ICC = 0.92–0.97; CV = 1.85–3.03) and very strong relationships inter-devices for the stand-up time (r = 0.94) and the total time (r = 0.98). The APE was lower than 6%, and the accuracy was higher than 94%. Based on our data, the findings suggest that the smartphone application is valid and reliable to collect the stand-up time and total time during the single sit-to-stand test with older adults.

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“…For these reasons, such algorithms are also more portable and can be executed directly on the devices that have battery constraints [25,26]. Following the research on the development of technological solutions to support healthy lifestyles, this team already developed systems for the recognition of activities of daily living [16][17][18][19][20], and measurement of jump flight time [21], energy expenditure [22], and other physical functional tests, including the heel rise test [23] and Timed Up and Go test [20,24].…”

Section: Studymentioning

confidence: 99%

Measurement of Results of Functional Reach Test with Sensors: A Systematic Review

2020

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The test of physical conditions is important to treat and presents several diseases related to the movement. These diseases are mainly related to the physiotherapy and orthopedy, but it can be applied in a wide range of medical specialties. The Functional Reach Test is one of the most common physical tests used to measure the limit of stability that is highly important for older adults because their stability is reduced with aging. Thus, older adults are part of the population more exposed to stroke. This test may help in the measurement of the conditions related to post-stroke and stroke treatment. The movements related to this test may be recorded and recognized with the inertial sensors available in off-the-shelf mobile devices. This systematic review aims to determine how to determine the conditions related to this test, which can be detected, and which of the sensors are used for this purpose. The main contribution of this paper is to present the research on the state-of-the-art use of sensors available on off-the-shelf mobile devices to measure Functional Reach Test results. This research shows that the sensors that are used in the literature studies are inertial sensors and force sensors. The features extracted from the different studies are categorized as dynamic balance, quantitative, and raw statistics. These features are mainly used to recognize the different parameters of the test, and several accidents, including falling. The execution of this test may allow the early detection of different diseases.

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Calculation of Jump Flight Time using a Mobile Device

Cited by 5 publications

References 12 publications

Identification of Activities of Daily Living Using Sensors Available in off-the-shelf Mobile Devices: Research and Hypothesis

Identification of Activities of Daily Living Using Sensors Available in off-the-shelf Mobile Devices: Research and Hypothesis

An Experimental Study on the Validity and Reliability of a Smartphone Application to Acquire Temporal Variables during the Single Sit-to-Stand Test with Older Adults

Measurement of Results of Functional Reach Test with Sensors: A Systematic Review

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