Wearable sensor grid architecture for body posture and surface detection and rehabilitation

Hermanis, Atis; Cacurs, Ričards; Nesenbergs, Krišjānis; Greitāns, Modris; Syundyukov, Emil; Selavo, Leo

doi:10.1145/2737095.2742555

Cited by 5 publications

(12 citation statements)

References 5 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%

“…There are five studies that didn’t provide evaluation evidence. Note that the availability of “evaluation evidence” was not used as an inclusion criterion in this study, in order to not exclude reports on very novel systems that are presented as proof of concept/principle as, for example, the smart fabric embedded wearable sensor grid discussed above [70]. Figure 7 Illustrates the systems evaluation status in details.

Fig.…”

Section: Resultsmentioning

confidence: 99%

“… Comparison accuracy test on structure. N/A ROM; Improve active joint range of motion; Arthritis Rehabiliation Hermanis et al 2015; [ 70 ] IMU (up to 200) Back KP Concurrent; 3D rendering surface showing the current posture of back or limb; Visual FB (Smartphone, Tablet or PC) None N/A Sensing fabric based on sensor grids; Embedded; normal wire Body Segment Posture; Improve posture; Bones trauma or General Rehabilitation Holden et al 2015 [ 71 ] Accel ( n = 2) Wrist KR Concurrent & Terminal; Training notification; Visual FB (Watch-like device); Vibrotactile FB; Usab. Usability test on patients ( n = 7) Watch-like device; Attachable; Amount of Use; Encourage arm hand use; Stroke Ongvisatepaiboon et al 2015; [ 72 ] Accel (Smartphone built-in) Elbow KP, KR Concurrent & Terminal; Shoulder joint angle value, repetition number; Visual FB (Smartphone) Tech.…”

Section: Resultsmentioning

confidence: 99%

“…Eighteen studies [ 4 , 39 , 45 , 52 , 54 – 56 , 59 , 61 , 65 , 67 – 70 , 75 , 76 , 78 , 79 ] applied IMUs in their systems, three [ 54 , 59 , 78 ] of them relied on a single sensor module. Most systems used 2–4 sensors, but studies that aimed for finger movement monitoring utilized more sensors [ 68 , 69 ].…”

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

282

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%

Fig.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

282

205

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Wearable System for Early Diagnosis and Follow Up of Spine Curvature Disorders

et al. 2019

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Investigating classification performance of hybrid deep learning and machine learning architectures on activity recognition

Uzunhisarcikli

Kavuncuoğlu

Özdemir

2022

Computational Intelligence

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Human activity recognition (HAR) has become a popular field to recognize people's activities from signals obtained using various types of body placed sensors. The increase in the elderly population will increase cognitive and physical decline due to aging and may cause to serious injuries and deaths if immediate assistance is not provided. For this reason, temporal dynamics and important features should automatically be extracted in order to support the daily life of the elderly and to recognize their physical activities correctly and real time. In the study, the classification performances of deep models (CNNLSTM: CNN long short-term memory network, ConvLSTM: convolutional LSTM, LSTM: long short-term memory network) and four other machine learning algorithms (SMVs: support vector machines, k-NN: k-nearest neighbor classifier, DT: decision tree classifier, ERF: ensemble random forest) which are known to be successful in HAR were investigated. Features used features used in deep models are automatically generated however in machine learning models were generated by hand. The deep models are developed using with a huge set of activities containing 2520 tests. In the tests, each activity of the volunteers was recorded with three axis accelerometer, gyroscope and magnetometer sensors placed in the waist region of 1402

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Wearable sensor grid architecture for body posture and surface detection and rehabilitation

Cited by 5 publications

References 5 publications

Interactive wearable systems for upper body rehabilitation: a systematic review

Interactive wearable systems for upper body rehabilitation: a systematic review

Wearable System for Early Diagnosis and Follow Up of Spine Curvature Disorders

Investigating classification performance of hybrid deep learning and machine learning architectures on activity recognition

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