Abstract
The smartphone and portable media device are equipped with inertial sensors, such as an accelerometer and gyroscope. With the proper software application they can function as wireless accelerometer and gyroscope platforms. This capability enables the smartphone and portable media device to function as wearable and wireless systems for gait and reflex response. The experimental trial data can be conveyed through wireless connectivity to the Internet as an email attachment for post-processing. The signal data can be further consolidated into a feature set for machine learning classification. Many experimental scenarios pertaining to quantifying the domains of gait and reflex response are presented. The smartphone and portable media device present an insightful perspective of the significant potential of Network Centric Therapy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
LeMoyne R, Mastroianni T (2015) Use of smartphones and portable media devices for quantifying human movement characteristics of gait, tendon reflex response, and Parkinson’s disease hand tremor. Methods and Protocols, Mobile Health Technologies, 335–358
LeMoyne R, Mastroianni T (2017) Wearable and wireless gait analysis platforms: smartphones and portable media devices. Wireless MEMS Networks and Applications, 129–152
LeMoyne R, Mastroianni T (2016) Telemedicine perspectives for wearable and wireless applications serving the domain of neurorehabilitation and movement disorder treatment. Telemedicine, 1–10
LeMoyne R (2016) Testing and evaluation strategies for the powered prosthesis, a global perspective. Advances for Prosthetic Technology: From Historical Perspective to Current Status to Future Application, 37–58
Patel S, Park H, Bonato P, Chan L, Rodgers M (2012) A review of wearable sensors and systems with application in rehabilitation. J Neuroengineering Rehabil 9(1):21
LeMoyne R, Coroian C, Mastroianni T, Grundfest W (2008) Accelerometers for quantification of gait and movement disorders: a perspective review. J Mech Med Biol 8(02):137–152
LeMoyne R, Coroian C, Cozza M, Opalinski P, Mastroianni T, Grundfest W (2009) The merits of artificial proprioception, with applications in biofeedback gait rehabilitation concepts and movement disorder characterization. Biomedical Engineering, 165–198
LeMoyne RC (2010) Wireless quantified reflex device. PhD Dissertation UCLA
LeMoyne R, Mastroianni T, Coroian C, Grundfest W (2011) Tendon reflex and strategies for quantification, with novel methods incorporating wireless accelerometer reflex quantification devices, a perspective review. J Mech Med Biol 11(03):471–513
LeMoyne R, Mastroianni T, Cozza M, Coroian C, Grundfest W (2010) Implementation of an iPhone as a wireless accelerometer for quantifying gait characteristics. In: 32nd Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 3847–3851
LeMoyne R, Mastroianni T, Cozza M, Coroian C (2010) iPhone wireless accelerometer application for acquiring quantified gait attributes. In: ASME 2010 5th Frontiers in Biomedical Devices Conference, American Society of Mechanical Engineers, pp 19–20
LeMoyne R, Mastroianni T, Cozza M, Coroian C (2010) Quantification of gait characteristics through a functional iPhone wireless accelerometer application mounted to the spine. In: ASME 2010 5th Frontiers in Biomedical Devices Conference, American Society of Mechanical Engineers, pp 87–88
LeMoyne R, Mastroianni T (2014) Quantification of patellar tendon reflex response using an iPod wireless gyroscope application with experimentation conducted in Lhasa, Tibet and post-processing conducted in Flagstaff, Arizona through wireless Internet connectivity. In: 44th Society for Neuroscience Annual Meeting
LeMoyne R, Coroian C, Mastroianni T, Grundfest W (2008) Virtual proprioception. J Mech Med Biol 8(03):317–338
LeMoyne R, Coroian C, Mastroianni T, Wu W, Grundfest W, Kaiser W (2008) Virtual proprioception with real-time step detection and processing. In: 30th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 4238–4241
LeMoyne R, Coroian C, Mastroianni T, Grundfest W (2009) Wireless accelerometer assessment of gait for quantified disparity of hemiparetic locomotion. J Mech Med Biol 9(03):329–343
LeMoyne R, Coroian C, Mastroianni T. Wireless accelerometer system for quantifying gait. In: ICME International Conference on IEEE, Complex Medical Engineering (CME), pp 1–4
LeMoyne R, Mastroianni T, Grundfest W (2013) Wireless accelerometer system for quantifying disparity of hemiplegic gait using the frequency domain. J Mech Med Biol 13(03):1350035
LeMoyne R, Mastroianni T, Grundfest W (2011) Wireless accelerometer iPod application for quantifying gait characteristics. In: 33rd Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 7904–7907
LeMoyne R, Mastroianni T (2014) Implementation of an iPod application as a wearable and wireless accelerometer system for identifying quantified disparity of hemiplegic gait. J Med Imaging Health Informatics 4(4):634–641
LeMoyne R, Mastroianni T, Montoya K (2014) Implementation of a smartphone for evaluating gait characteristics of a trans-tibial prosthesis. In: 36th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 3674–3677
LeMoyne R, Mastroianni T (2016) Implementation of a smartphone as a wireless gyroscope platform for quantifying reduced arm swing in hemiplegic gait with machine learning classification by multilayer perceptron neural network. In: 38th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 2626–2630
Mastroianni T, LeMoyne R (2016) Application of a multilayer perceptron neural network with an iPod as a wireless gyroscope platform to classify reduced arm swing gait for people with Erb’s palsy. In: 46th Society for Neuroscience Annual Meeting
LeMoyne R, Mastroianni T, Grundfest W (2012) Quantified reflex strategy using an iPod as a wireless accelerometer application. In: 34th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 2476–2479
LeMoyne R, Mastroianni T (2011) Reflex response quantification using an iPod wireless accelerometer application. In: 41st Society for Neuroscience Annual Meeting
LeMoyne R, Kerr WT, Zanjani K, Mastroianni T (2014) Implementation of an iPod wireless accelerometer application using machine learning to classify disparity of hemiplegic and healthy patellar tendon reflex pair. J Med Imaging Health Inform 4(1):21–28
LeMoyne R, Mastroianni T, Grundfest W, Nishikawa K (2013) Implementation of an iPhone wireless accelerometer application for the quantification of reflex response. In: 35th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 4658–4661
LeMoyne R, Mastroianni T(2014) Implementation of a smartphone as a wireless gyroscope application for the quantification of reflex response. In: 36th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 3654–3657
LeMoyne R, Mastroianni T (2015) Machine learning classification of a hemiplegic and healthy patellar tendon reflex pair through an iPod wireless gyroscope platform. In: 45th Society for Neuroscience Annual Meeting
LeMoyne R, Mastroianni T (2016) Implementation of a multilayer perceptron neural network for classifying a hemiplegic and healthy reflex pair using an iPod wireless gyroscope platform. In: 46th Society for Neuroscience Annual Meeting
LeMoyne R, Mastroianni T (2016) Smartphone wireless gyroscope platform for machine learning classification of hemiplegic patellar tendon reflex pair disparity through a multilayer perceptron neural network. In: Wireless Health (WH) of IEEE, pp 1–6
LeMoyne R, Mastroianni T (2017) Implementation of a smartphone wireless gyroscope platform with machine learning for classifying disparity of a hemiplegic patellar tendon reflex pair. J Mech Med Biol (Online Ready):1750083
Furrer M, Bichsel L, Niederer M, Baur H, Schmid S (2015) Validation of a smartphone-based measurement tool for the quantification of level walking. Gait Posture. 42(3):289–294
Nishiguchi S, Yamada M, Nagai K, Mori S, Kajiwara Y, Sonoda T, Yoshimura K, Yoshitomi H, Ito H, Okamoto K, Ito T (2012) Reliability and validity of gait analysis by android-based smartphone. Telemedicine e-Health 18(4):292–296
Pluijter N, de Wit LP, Bruijn SM, Plaisier MA (2015) Tactile pavement for guiding walking direction: an assessment of heading direction and gait stability. Gait Posture 42(4):534–538
Cerrito A, Bichsel L, Radlinger L, Schmid S (2015) Reliability and validity of a smartphone-based application for the quantification of the sit-to-stand movement in healthy seniors. Gait Posture 41(2):409–413
Mellone S, Tacconi C, Chiari L (2012) Validity of a smartphone-based instrumented timed up and go. Gait Posture 36(1):163–165
Capela NA, Lemaire ED, Baddour N (2015) Novel algorithm for a smartphone-based 6-minute walk test application: algorithm, application development, and evaluation. J Neuroengineering Rehabil 12(1):19
Galán-Mercant A, Barón-López FJ, Labajos-Manzanares MT, Cuesta-Vargas AI (2014) Reliability and criterion-related validity with a smartphone used in timed-up-and-go test. Biomed Eng Online 13(1):156
Juen J, Cheng Q, Schatz B (2015) A natural walking monitor for pulmonary patients using mobile phones. IEEE J Biomed Health Inform 19(4):1399–1405
Galán-Mercant A, Cuesta-Vargas AI (2013) Differences in trunk accelerometry between frail and nonfrail elderly persons in sit-to-stand and stand-to-sit transitions based on a mobile inertial sensor. JMIR mhealth uhealth 1(2):e21
Galán-Mercant A, Cuesta-Vargas AI (2014) Differences in trunk accelerometry between frail and non-frail elderly persons in functional tasks. BMC Res Notes 7(1):100
Fontecha J, Hervás R, Bravo J, Navarro FJ (2013) A mobile and ubiquitous approach for supporting frailty assessment in elderly people. J Med Internet Res 15(9):e197
Hewson DJ, Jaber R, Chkeir A, Hammoud A, Gupta D, Bassement J, Vermeulen J, Yadav S, de Witte L, Duchêne J (2013) Development of a monitoring system for physical frailty in independent elderly. In: 35th annual international conference of the IEEE, Engineering in Medicine and Biology Society (EMBC), pp 6215–6218
Yamada M, Aoyama T, Okamoto K, Nagai K, Tanaka B, Takemura T (2011) Using a smartphone while walking: a measure of dual-tasking ability as a falls risk assessment tool. Age Ageing (afr039)
Isho T, Tashiro H, Usuda S (2015) Accelerometry-based gait characteristics evaluated using a smartphone and their association with fall risk in people with chronic stroke. J Stroke Cerebrovasc Dis 24(6):1305–1311
Juen J, Cheng Q, Prieto-Centurion V, Krishnan JA, Schatz B (2014) Health monitors for chronic disease by gait analysis with mobile phones. Telemedicine e-Health 20(11):1035–1041
Yamada M, Aoyama T, Mori S, Nishiguchi S, Okamoto K, Ito T, Muto S, Ishihara T, Yoshitomi H, Ito H (2012) Objective assessment of abnormal gait in patients with rheumatoid arthritis using a smartphone. Rheumatol Int 32(12):3869–3874
Nishiguchi S, Ito H, Yamada M, Yoshitomi H, Furu M, Ito T, Shinohara A, Ura T, Okamoto K, Aoyama T (2014) Self-assessment tool of disease activity of rheumatoid arthritis by using a smartphone application. Telemedicine e-Health 20(3):235–240
Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, Little MA (2015) Detecting and monitoring the symptoms of Parkinson’s disease using smartphones: a pilot study. Parkinsonism Relat Disord 21(6):650–653
Raknim P, Lan KC (2016) Gait monitoring for early neurological disorder detection using sensors in a smartphone: validation and a case study of parkinsonism. Telemedicine e-Health 22(1):75–81
Ellis RJ, Ng YS, Zhu S, Tan DM, Anderson B, Schlaug G, Wang Y (2015) A validated smartphone-based assessment of gait and gait variability in Parkinson’s disease. PLoS One 10(10):e0141694
Takač B, Català A, Martín DR, Van Der Aa N, Chen W, Rauterberg M (2013) Position and orientation tracking in a ubiquitous monitoring system for Parkinson disease patients with freezing of gait symptom. JMIR mHealth uHealth 1(2):e14
Pan D, Dhall R, Lieberman A, Petitti DB (2015) A mobile cloud-based parkinson’s disease assessment system for home-based monitoring. JMIR mHealth uHealth 3(1):e29
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
LeMoyne, R., Mastroianni, T. (2018). Smartphones and Portable Media Devices as Wearable and Wireless Systems for Gait and Reflex Response Quantification. In: Wearable and Wireless Systems for Healthcare I. Smart Sensors, Measurement and Instrumentation, vol 27. Springer, Singapore. https://doi.org/10.1007/978-981-10-5684-0_6
Download citation
DOI: https://doi.org/10.1007/978-981-10-5684-0_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5683-3
Online ISBN: 978-981-10-5684-0
eBook Packages: EngineeringEngineering (R0)