Measurement of the Reaction Time in the 30-S Chair Stand Test using the Accelerometer Sensor Available in off-the-Shelf Mobile Devices

The 30-s Chair Stand Test (CST) is commonly used with elderly people for assessing the lower limbs strength, which can provide sufficient information regarding the general mobility and fall risk. The mobile devices are widely used for the acquisition of the different physical and physiological data from the sensors available, including the accelerometer. In this way, the aim of the present study consisted on the development of an automatic method for the measurement of the reaction time (RT) based on the 30-s CST using a mobile device. Besides that, the data acquisition through an accelerometer allows the assessment of different variables, such as the maximum values of the acceleration, the instant velocity, the maximum force and the peak power, that may contribute to a better understanding of the physical demands during the 30-s CST performance. The results presented in this study demonstrated that the calculation of the RT and the different variables during the 30-s CST performance is possible, opening new possibilities for the development of scientific projects, namely those that encompasses the motor and cognitive training of

[1]  Erin E. Flynn-Evans,et al.  Measurement of Visual Reaction Times Using Hand-held Mobile Devices , 2016 .

[2]  Nuno M. Garcia,et al.  From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices , 2016, Sensors.

[3]  Roozbeh Jafari,et al.  Motion Based Acceleration Correction for Improved Sensor Orientation Estimates , 2014, 2014 11th International Conference on Wearable and Implantable Body Sensor Networks.

[4]  Nuno M. Garcia,et al.  Elderly mobility analysis during Timed Up and Go test using biosignals , 2016, DSAI.

[5]  W. Beam,et al.  A 30-s chair-stand test as a measure of lower body strength in community-residing older adults. , 1999, Research quarterly for exercise and sport.

[6]  J. Walston,et al.  Sarcopenia in older adults , 2012, Current opinion in rheumatology.

[7]  Ellen Gorus,et al.  Age-related differences in muscle recruitment and reaction-time performance , 2015, Experimental Gerontology.

[8]  Diana Baader,et al.  Senior Fitness Test Manual , 2016 .

[9]  Nuno M. Garcia A Roadmap to the Design of a Personal Digital Life Coach , 2015, ICT Innovations.

[10]  Wiebren Zijlstra,et al.  Sensitivity of sensor-based sit-to-stand peak power to the effects of training leg strength, leg power and balance in older adults. , 2014, Gait & posture.

[11]  Nuno M. Garcia,et al.  Identification of Activities of Daily Living Using Sensors Available in off-the-shelf Mobile Devices: Research and Hypothesis , 2016, ISAmI.

[12]  Aditya Jain,et al.  A comparative study of visual and auditory reaction times on the basis of gender and physical activity levels of medical first year students , 2015, International journal of applied & basic medical research.

[13]  Ellen Gorus,et al.  Age-related differences in pre-movement antagonist muscle co-activation and reaction-time performance , 2011, Experimental Gerontology.

[14]  Nora Millor,et al.  Gait Velocity and Chair Sit-Stand-Sit Performance Improves Current Frailty-Status Identification , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  J. Tihanyi,et al.  Force-velocity-power characteristics and fiber composition in human knee extensor muscles , 1982, European Journal of Applied Physiology and Occupational Physiology.

[16]  Raymond J. Seeger Newton's Second Law , 1962 .

[17]  Ricardo Costa,et al.  Ambient Assisted Living , 2009 .

[18]  John W. Krakauer,et al.  Motor Planning , 2015, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[19]  Mário C. Marques,et al.  Effects of high-speed power training on functional capacity and muscle performance in older women , 2012, Experimental Gerontology.

[20]  Juan A. Botía Blaya,et al.  Ambient Assisted Living system for in-home monitoring of healthy independent elders , 2012, Expert Syst. Appl..

[21]  Nuno M. Garcia,et al.  Calculation of Jump Flight Time using a Mobile Device , 2015, HEALTHINF.

[22]  N. Garcia,et al.  Multi-sensor data fusion techniques for the identification of activities of daily living using mobile devices , 2015 .