Comparison of four different mobile devices for measuring heart rate and ECG with respect to aspects of usability and acceptance by older people

2012 ◽  
Vol 43 (3) ◽  
pp. 582-587 ◽  
Author(s):  
Hilko Ehmen ◽  
Marten Haesner ◽  
Ines Steinke ◽  
Mario Dorn ◽  
Mehmet Gövercin ◽  
...  
Keyword(s):  
Heart Rate ◽  
Older People ◽  
Mobile Devices

Framing the Design Space of Multimodal Mid-Air Gesture and Speech-Based Interaction With Mobile Devices for Older People

2020 ◽  
Vol 12 (1) ◽  
pp. 22-41
Author(s):  
Ornella Mich ◽  
Gianluca Schiavo ◽  
Michela Ferron ◽  
Nadia Mana
Keyword(s):  
Older Adults ◽  
Older People ◽  
Mobile Devices ◽  
Mobile Technology ◽  
Design Space ◽  
Multimodal Interaction ◽  
Voice Input ◽  
Multiple Interaction ◽  
Technology Users ◽  
Key Aspects

Multimodal human–computer interaction has been sought to provide not only more compelling interactive experiences, but also more accessible interfaces to mobile devices. With the advance in mobile technology and in affordable sensors, multimodal research that leverages and combines multiple interaction modalities (such as speech, touch, vision, and gesture) has become more and more prominent. This article provides a framework for the key aspects in mid-air gesture and speech-based interaction for older adults. It explores the literature on multimodal interaction and older adults as technology users and summarises the main findings for this type of users. Building on these findings, a number of crucial factors to take into consideration when designing multimodal mobile technology for older adults are described. The aim of this work is to promote the usefulness and potential of multimodal technologies based on mid-air gestures and voice input for making older adults' interaction with mobile devices more accessible and inclusive.

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

Computers ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 67
Author(s):  
Vasco Ponciano ◽  
Ivan Miguel Pires ◽  
Fernando Reinaldo Ribeiro ◽  
María Vanessa Villasana ◽  
Maria Canavarro Teixeira ◽  
...  
Keyword(s):  
Heart Rate ◽  
Experimental Study ◽  
Heart Rate Variability ◽  
Mobile Devices ◽  
Brain Activity ◽  
Health Conditions ◽  
Qrs Complex ◽  
Timed Up And Go ◽  
Eeg Data ◽  
Ecg Data

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, linear 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.

Comparison of three mobile devices for measuring R–R intervals and heart rate variability: Polar S810i, Suunto t6 and an ambulatory ECG system

2010 ◽  
Vol 109 (4) ◽  
pp. 779-786 ◽  
Author(s):  
Matthias Weippert ◽  
Mohit Kumar ◽  
Steffi Kreuzfeld ◽  
Dagmar Arndt ◽  
Annika Rieger ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Mobile Devices ◽  
Ambulatory Ecg

scholarly journals Serotonin in the elderly

2020 ◽  
pp. 79-81
Author(s):  
OBW Greeff
Keyword(s):  
Heart Rate ◽  
Older People ◽  
Clinical Effectiveness ◽  
The Elderly ◽  
Tricyclic Antidepressant ◽  
The Body ◽  
Biological Functions ◽  
Emotional Wellbeing ◽  
Serotonergic Function ◽  
Norepinephrine Reuptake

Serotonin is said to determine our emotions, moods and affect. The physiology of serotonin implies it in the regulation of biological functions in line with our mood, like accelerated heart rate and sweating triggered by feeling flustered. Serotonin has multiple pharmacological actions in the body and alterations in serotonergic function may account for behavioural disturbances commonly observed in the elderly. The clinical effectiveness of selective 5-HT reuptake inhibitors (SSRIs) is believed to be mediated through enhancement of extracellular 5 HT. Various studies have shown that the SSRIs are better tolerated in older people compared with the tricyclic antidepressant or serotonin-norepinephrine reuptake inhibitors (SNRIs). It is obvious that serotonin plays a significant role in the emotional wellbeing of people – whether they are young or old and there are various ways of increasing serotonin in the body in order to create a feeling of wellbeing and content.

scholarly journals The effects of exergaming on pain, postural control, technology acceptance and flow experience in older people with chronic musculoskeletal pain: a randomised controlled trial

2020 ◽  
Author(s):  
Jae-Llane Ditchburn ◽  
Paul van Schaik ◽  
John Dixon ◽  
Alasdair MacSween ◽  
Denis Martin
Keyword(s):  
Heart Rate ◽  
Older People ◽  
Postural Control ◽  
Technology Acceptance ◽  
Musculoskeletal Pain ◽  
Physical Exertion ◽  
Mental Effort ◽  
Chronic Musculoskeletal Pain ◽  
Flow Experience ◽  
Over Time

Abstract Background: Older people with chronic musculoskeletal pain are at risk of falls. This study aimed to investigate the effects of exergaming on pain and postural control in older people with chronic musculoskeletal pain. Secondary outcomes were technology acceptance, flow experience, perceived physical exertion, expended mental effort and heart rate. Methods: 54 older adults (age: 71 ± 5 years) with chronic musculoskeletal pain were randomised into 2 groups. Group 1 received exergaming training using the Interactive Rehabilitation and Exercise System (IREX®). Group 2 undertook traditional gym-based exercise (TGB). Both groups completed twice weekly 40-minute exercise sessions for six weeks. Perceived pain was measured using a numeric pain rating scale and the Multidimensional Affect and Pain Survey questionnaire. Postural control was measured as sway using a Kistler™ force platform. Technology acceptance was measured with the Unified Theory of Acceptance and Use of Technology questionnaire and flow experience with the Flow State Scale. Physiological measures of perceived physical exertion, expended mental effort and heart rate were recorded during all sessions. Results: The exergaming group demonstrated significant reductions in pain intensity and thermal pain including a near significant approach in physical engagement in comparison to TGB group. Although no intervention effects on postural control were found, the exergaming group showed significant improvements in three sway measures (AP SD, ML SD and AP range) over time whereas significant improvements in ML range were found in the TGB group. Relating to technology acceptance, significant intervention effects on social influence and behavioural intention were found in the TGB group instead, although both groups demonstrated increases of acceptance over time. Regarding flow experience, concentration at task was significantly influenced in the TGB group and significant increases in flow variables over time were observed in both groups. Significant increases over time in perceived physical exertion and expended mental effort were found in both groups. Conclusion: Our findings support the potential of exergaming to alleviate pain and improve balance in older people with chronic musculoskeletal pain. Both forms of exercise are acceptable, intrinsically motivating and show evidence of benefit to older people with chronic musculoskeletal pain. Trial registration: ClinicalTrials.gov Identifier: NCT04029285 (retrospectively registered, July 23, 2019) https://clinicaltrials.gov/ct2/show/study/NCT04029285?term=Alasdair+Macsween&draw=2&rank=2

An Investigation on the Use of Mobile Devices Among Older People

2015 ◽  
Vol 21 (10) ◽  
pp. 3146-3150 ◽  
Author(s):  
Sofianiza Abd Malik ◽  
Muna Azuddin ◽  
Lili Marziana Abdullah ◽  
Murni Mahmud
Keyword(s):  
Older People ◽  
Mobile Devices

The Influence Factors of Mobile Devices Adoption Among Older People

2018 ◽  
Vol 9 (3) ◽  
pp. 20-33
Author(s):  
Muna Azuddin ◽  
Sofianiza Abd Malik ◽  
Lili Marziana Abdullah
Keyword(s):  
Older People ◽  
Focus Groups ◽  
Qualitative Methods ◽  
Small Group ◽  
Mobile Devices ◽  
Mobile Device ◽  
Influence Factors ◽  
Mobile Users ◽  
Related Factors ◽  
Five Factors

This article describes how the use of mobile devices has huge potential in assisting mobile users to perform everyday tasks. However, there is a small percentage of older people that uses mobile devices. This shows that, there are several factors that influenced the small group of older people to adopt the technology. In addition, there have been limited studies that investigate factors that influenced older people to adopt mobile devices. This article aims to understand the causes that have encouraged older people to adopt such technology. Qualitative methods including interview and focus groups were conducted among twenty-one older people aged 55 and above. The results indicated that, there were five factors that influenced older people to use mobile devices. These factors were mobile device design, functions, social inspiration, economical and other related factors.

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