Integration of tilt sensors as a device for monitoring rehabilitation process

Salim, Safyzan; Zakaria, Wan Nurshazwani Wan; Nizhan, Azrai; Jamil, M. Mahadi Abdul

doi:10.1109/iccsce.2014.7072721

Cited by 5 publications

(9 citation statements)

References 10 publications

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“…)[4, 40, 44, 48, 50, 60, 63–65, 72, 75, 77, 79]3D model of limb or human body or structure[37, 41, 43, 46, 53, 56, 68–70]Game[42, 45, 59, 62, 66, 67]HapticVibrotactile display[51, 52, 57, 61]AuditoryMusical pattern[78]Multi-modal[3, 38, 39, 47, 49, 54, 55, 58, 71, 73, 74, 76] …”

Section: Resultsmentioning

confidence: 99%

“…Our results show that virtual reality has been commonly used within the included studies (three studies [37–39] in 2010, two [45, 46] in 2011, one [56] in 2013, three [62, 67, 68] in 2014 and one [69] in 2015). Further to using a computer screen as a visual feedback display, the emergence of smartphones is reflected on the number of the systems providing feedback on smartphones: 0 in 2010–2012, two [54, 58] in 2013, five [59, 60, 63, 64, 66] in 2014, four [70, 72–74] in 2015 and two [76, 77] in 2016.…”

Section: Resultsmentioning

confidence: 99%

“… Usability test withnormal people ( n = 16) Sleeve; Embedded; Conductive ribbon Body Segment Posture & Speed; Improve movement performance; General Rehabilitation(arm) Rahman et al 2014; [ 62 ] LDR sensor Hand KR Terminal; Game score on arm movement; VR (game) Tech. Accuracy and sensitivity test on normal people ( n = 2) Fabric Glove; Embedded; Normal wire Body Segment Posture; Improve movement performance; Cerebral Palsy Salim et al 2014; [ 63 ] Tilt sensor ( n = 2) Hand KP Concurrent; Indication of arm movement direction; Visual FB (Smartphone) Tech. Pilot experiment ( n = 1) Fabric Glove; Embedded; Normal wire Body Segment Posture; Improve movement performance; Stroke Friedman et al 2014; [ 64 ] Magnetometer ( n = 2), Accel ( n = 1) Wrist, finger KP Concurrent; Amount of use of wrist and finger; Visual FB (Tablet) Tech.…”

Section: Resultsmentioning

confidence: 99%

“…Besides, some researchers explored other metrics. Rahman et al [ 62 ] and Salim et al [ 63 ] proposed a glove-based motion detecting system by integrating LDR sensors and tilt sensors separately.…”

Section: Resultsmentioning

confidence: 99%

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Interactive wearable systems for upper body rehabilitation: a systematic review

Wang

Markopoulos

et al. 2017

J NeuroEngineering Rehabil

280

205

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BackgroundThe development of interactive rehabilitation technologies which rely on wearable-sensing for upper body rehabilitation is attracting increasing research interest. This paper reviews related research with the aim: 1) To inventory and classify interactive wearable systems for movement and posture monitoring during upper body rehabilitation, regarding the sensing technology, system measurements and feedback conditions; 2) To gauge the wearability of the wearable systems; 3) To inventory the availability of clinical evidence supporting the effectiveness of related technologies.MethodA systematic literature search was conducted in the following search engines: PubMed, ACM, Scopus and IEEE (January 2010–April 2016).ResultsForty-five papers were included and discussed in a new cuboid taxonomy which consists of 3 dimensions: sensing technology, feedback modalities and system measurements. Wearable sensor systems were developed for persons in: 1) Neuro-rehabilitation: stroke (n = 21), spinal cord injury (n = 1), cerebral palsy (n = 2), Alzheimer (n = 1); 2) Musculoskeletal impairment: ligament rehabilitation (n = 1), arthritis (n = 1), frozen shoulder (n = 1), bones trauma (n = 1); 3) Others: chronic pulmonary obstructive disease (n = 1), chronic pain rehabilitation (n = 1) and other general rehabilitation (n = 14). Accelerometers and inertial measurement units (IMU) are the most frequently used technologies (84% of the papers). They are mostly used in multiple sensor configurations to measure upper limb kinematics and/or trunk posture. Sensors are placed mostly on the trunk, upper arm, the forearm, the wrist, and the finger. Typically sensors are attachable rather than embedded in wearable devices and garments; although studies that embed and integrate sensors are increasing in the last 4 years. 16 studies applied knowledge of result (KR) feedback, 14 studies applied knowledge of performance (KP) feedback and 15 studies applied both in various modalities. 16 studies have conducted their evaluation with patients and reported usability tests, while only three of them conducted clinical trials including one randomized clinical trial.ConclusionsThis review has shown that wearable systems are used mostly for the monitoring and provision of feedback on posture and upper extremity movements in stroke rehabilitation. The results indicated that accelerometers and IMUs are the most frequently used sensors, in most cases attached to the body through ad hoc contraptions for the purpose of improving range of motion and movement performance during upper body rehabilitation. Systems featuring sensors embedded in wearable appliances or garments are only beginning to emerge. Similarly, clinical evaluations are scarce and are further needed to provide evidence on effectiveness and pave the path towards implementation in clinical settings.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Interactive wearable systems for upper body rehabilitation: a systematic review

Wang

Markopoulos

et al. 2017

J NeuroEngineering Rehabil

280

205

View full text Add to dashboard Cite

show abstract

“…Therefore, various types of sensors are used to design low cost, home-based physical rehabilitation systems. For example, IMU [6] sensor, tilt sensor [24], and camera [34] based systems are becoming popular. Even, people have started designing software tools for hand-held devices such as smart phones, tablets [19], [25] to support healthcare services since these devices are equipped with additional sensors.…”

Section: Introductionmentioning

confidence: 99%

Computer-Vision-Assisted Palm Rehabilitation With Supervised Learning

Vamsikrishna

Dogra

Desarkar

2016

IEEE Trans. Biomed. Eng.

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Physical rehabilitation supported by the computer-assisted-interface is gaining popularity among health-care fraternity. In this paper, we have proposed a computer-vision-assisted contactless methodology to facilitate palm and finger rehabilitation. Leap motion controller has been interfaced with a computing device to record parameters describing 3-D movements of the palm of a user undergoing rehabilitation. We have proposed an interface using Unity3D development platform. Our interface is capable of analyzing intermediate steps of rehabilitation without the help of an expert, and it can provide online feedback to the user. Isolated gestures are classified using linear discriminant analysis (DA) and support vector machines (SVM). Finally, a set of discrete hidden Markov models (HMM) have been used to classify gesture sequence performed during rehabilitation. Experimental validation using a large number of samples collected from healthy volunteers reveals that DA and SVM perform similarly while applied on isolated gesture recognition. We have compared the results of HMM-based sequence classification with CRF-based techniques. Our results confirm that both HMM and CRF perform quite similarly when tested on gesture sequences. The proposed system can be used for home-based palm or finger rehabilitation in the absence of experts.

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