Position-sensing technologies for movement analysis in stroke rehabilitation

Zheng, Huiru; Black, Norman D.; Harris, Nigel

doi:10.1007/bf02344720

Cited by 141 publications

(86 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This 2.2°error does not exceed the 5°mean error limit established by the American Medical Association for reliable evaluation of movement impairments in a clinical context and is therefore clinically insignificant. 24 Furthermore, the fast Fourier transform analysis revealed that the iPhone app and dynamometer recorded the same dominant frequencies during rotation of the dynamometer's movement arm. Finally, the iPhone app demonstrated a high level of precision, with the coefficients of variation measuring less than 3% for all speeds except 150°/s (6.8%).…”

Section: Discussionmentioning

confidence: 96%

Accuracy and Precision of an Accelerometer-Based Smartphone App Designed to Monitor and Record Angular Movement over Time

Bittel

Elazzazi

Bittel

2016

Telemedicine and e-Health

View full text Add to dashboard Cite

"Accuracy and precision of an accelerometer-based smartphone app designed to monitor and record angular movement over time. " Telemedicine and e-Health.22,4. 302-309. (2016 AbstractBackground: Therapeutic exercise is a central component in the management of many common conditions. It is imperative, therefore, that clinicians monitor and correct patient performance to facilitate the use of proper form both in the clinic and during home exercise programs. Although clinicians are trained to prescribe exercise and analyze form, there are many subtleties that may be missed by relying on visual assessment. This study investigated the accuracy and precision of a novel, exercise-training smartphone application (app), running on an iPhone Ò (Apple, Cupertino, CA) 4

show abstract

Section: Discussionmentioning

confidence: 96%

Accuracy and Precision of an Accelerometer-Based Smartphone App Designed to Monitor and Record Angular Movement over Time

Bittel

Elazzazi

Bittel

2016

Telemedicine and e-Health

View full text Add to dashboard Cite

show abstract

“…In particular, in 5-10 years, the gaming scenario will probably be dominated by games where the user will interactively control virtual characters by using some easily wearable devices (at most 4-5 on the whole body, e.g., possible locations could include feet, ankles, wrists, and head [15,17,33]). The medical scenario will instead begin to adopt wearable sensors able to assess health of the patients by remotely measuring vital signs/parameters and allowing patients to freely stay at their homes still keeping a direct connection with their doctors [5,7,18,23,24,25,29,37,38,41,42].…”

Section: Future Directionsmentioning

confidence: 99%

“…This is also reflected by various efforts already spent in developing mHealth applications based, for instance, on the use of inertial sensors [5,23,24,25,37,41].…”

Section: Introductionmentioning

confidence: 99%

Motion Capture: From Radio Signals to Inertial Signals

Giuberti

Ferrari

2015

Mobile Health

View full text Add to dashboard Cite

The study of the motion of individuals allows to gather relevant information on a person status, to be used in several fields (e.g., medical, sport, and entertainment). Over the past decade, the research activity in motion capture has benefited from the progress of portable and mobile sensors, paving the way toward the use of motion capture techniques in mHealth applications (e.g., remote monitoring of patients, and telerehabilitation). Indeed, even if the optical motion capture, which typically relies on a set of fixed cameras and body-worn reflecting markers, is generally perceived as the standard reference approach, other motion capture techniques, such as radio and inertial, are attracting an increasing attention because of their suitability in remote mHealth applications. Moreover, several hybrid approaches have been studied and proposed in order to overcome the limitations of component technologies considered independently. In this chapter, we present an overview of possible integration strategies between radio and inertial motion capture techniques. We start by investigating a radio-based approach, based on the fingerprinting radio localization technique. Then, the previous approach is improved by integrating inertial measurements: namely, accelerometers are used to provide an estimate of the nodes' pitches. Finally, the radio signals are abandoned in favor of only inertial measurements (obtained through accelerometers, gyroscopes, and magnetometers). The advantages and limitations of all approaches are discussed in a comparative way, characterizing the similarities and differences between the various approaches.

show abstract

“…In particular, there are now novel sensors and mobile devices that can be used to measure and monitor a variety of human activities in the home -for example, for the detection of the number of steps taken, the detection of sitting and standing postures, or the precise angle between the upper and lower arm of a person stretching their arm [3,4,9]. There is much potential for using these versatile and lightweight technologies in the development of augmented rehabilitation therapy for people with stroke [10].…”

Section: Potential Of Novel Technologiesmentioning

confidence: 99%