Mobile phone sensors in health applications

Stankevich, Evgeny; Paramonov, Ilya; Timofeev, Ivan

doi:10.23919/fruct.2012.8122097

Cited by 28 publications

(15 citation statements)

References 12 publications

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“…These sensors include GPS sensors, audio sensors (microphones), image sensors (cameras), light sensors, temperature sensors, direction sensors (compasses) and acceleration sensors (accelerometers) [1]. Recently, researchers have implemented several mobile sensing applications, in variety of domains including healthcare, social networks, safety, and environmental monitoring [2]. Activity recognition model permits us to gain comprehensive information about the daily activity which users have performed.…”

Section: Introductionmentioning

confidence: 99%

Discriminative time-domain features for activity recognition on a mobile phone

Büber

Güvensan

2014

2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

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People perform several activities during the daily life. It is important to reveal and analyze the daily life characteristic of a person, since it might help to cure several health problems. Especially to overcome obesity, heart attacks etc., people frequently do exercise. However, it is not easy to calculate the consumed energy during these exercises. Extra devices were/are required accomplishing this task. On the other hand, the powerful mobile phones encourage researchers to implement activity recognition task on these smartphones. Thus, activity recognition via mobile phone applications became so popular that several publications are made within the last five years. In this study, we elaborate on the discriminative timedomain features in order to recognize the daily activities with reduced number of features. 70 features, combined from existing studies have been analyzed and 15 of them are selected for the implementation of activity recognition on mobile phone. 6 different classification algorithms and 2 feature selection algorithms have been tested comparatively. The test results show that 8 daily activities including walking, sitting, standing, ascending/descending stairs, jogging, cycling and jumping could be classified with 94% ratio of success rate. Since k-NN is one of the most successful classifier, we have implemented it on our mobile application.

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Section: Introductionmentioning

confidence: 99%

Discriminative time-domain features for activity recognition on a mobile phone

Büber

Güvensan

2014

2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

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“…Sensors: Types, Environments and Position. Source: Stankevich et al, [9]. The main idea of measuring the heart rate using the mobile phone is to detect variations in the color of the skin of the finger and the brightness that occur due to the pulsation of the blood using the camera of the smart phone.…”

Section: Resultsmentioning

confidence: 99%

“…Category Use of sensors in applications, apps in health: The main reason for using mobile phones in the health domain is to improve the quality and availability of health services, that many people in the world already have a mobile phone. Solutions based on mobile phones can reduce the costs of health services and is another reason to use them [9].…”

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confidence: 99%

A Systematic Review of mHealth apps Evaluations for Cardiac Issues

et al. 2018

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Currently, with the widespread penetration of mobile devices with Internet access, including the smartphones, they can allow specific and/or complementary activities in the health field as well as in other commercial sectors. To verify the impact of the studies about medical scientific publications relatives to cardiac mobile applications (app). This review corresponds to information in scientific journals of high impact. The intention of this review is respond to the follow question: How these research works have evaluated the performance of health mobile applications, with a special interest in cardiac issues? This review of these searches corresponds to an analysis by 4 categories, which are: Assessment of the wearables—Body Care, Use of sensors in the applications, app in health, Health Care—Comparisons of review app and app specialized commercial/clinical use. Only 6% of the app are associated with a medical professional, 15% is published by a professional medical society and 63% according to the opinions of the user. It provides evidence of how some apps have been evaluated, and in some cases the effectiveness of the estimated accuracy is not in line with the real situation. In Panama, a platform has been presented that permit the integration of health applications for patient follow-up. AmI-HEALTH has been developed to provide a mechanism for self-management of hypertensive patient data, by re-cording elements such as blood pressure (systolic, diastolic and pulse). In this point is very important to remember to the near relation between the Cardiac Issues and the hypertension condition. This makes us reflect on the true implications that bring us closer to these technological innovations. Today, our world is so changing and globalized.

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“…The motion data was obtained from the mobile phone accelerometer that provided suitable for assessing how the exercise is performed. The current development takes advantage of modern mobile phones that includes a wide range of sensors such as a microphone, camera and accelerometers [20]. The multimedia development focused on the GUI elements, the interface presents the user with a home menu showing the human body, from it, the user can select what body part he or she wants to exercise.…”

Section: B Developmentmentioning

confidence: 99%

Lower member complimentary exercise gaming app

Prada-Dominguez

Valdivia-Trujillo

Uribe-Quevedo

et al. 2014

2014 IEEE Colombian Conference on Communications and Computing (COLCOM)

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The development and use of devices for monitoring physical activity is largely growing due to size reduction, affordability and gamified mechanics that are encouraging user in augment their experience. Some examples can be seen in commercial devices targeted at healthy user who wish to improve their fitness, however, such developments are also growing as technological devices that allow quantifying the preventive or corrective physical therapy with engaging virtual scenarios that provide an immersive experience. Current advances in mechanical, electronics and computer systems are allowing that enthusiasts and researchers take advantage of low cost technologies integrating solutions with devices such as, tablets, smartphones, laptops and gaming consoles. The assessment of the user with these tools provide data that allows following up the patient's evolution during the therapy from the user and health care specialist point of view complementing visual and survey-based assessments. This project proposes an application for monitoring lower member physical activity while the user performs targeted movements within a gamified system, resulting in point rewards that increase or decrease accordingly to the correct or incorrect execution of the routine. The use of game mechanics allows overcoming some of the challenges of performing exercise such as the lack of interest and subjective evaluations.

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Mobile phone sensors in health applications

Cited by 28 publications

References 12 publications

Discriminative time-domain features for activity recognition on a mobile phone

Discriminative time-domain features for activity recognition on a mobile phone

A Systematic Review of mHealth apps Evaluations for Cardiac Issues

Lower member complimentary exercise gaming app

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