Sharif-human movement instrumentation system (SHARIF-HMIS) for daily activities

Mokhlespour, M. I.; Zobeiri, Omid A.; Akbari, Ali; Milani, Y.; Narimani, Roya; Moshiri, Behzad; Parnianpour, Mohamad

doi:10.1109/icbme.2012.6519674

Cited by 7 publications

(7 citation statements)

References 3 publications

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“…The captured data was transmitted to a PC-based data logger using an AVR microcontroller (Atmel Corp., San Jose, CA, USA). The technical information on the SHARIF-HMIS system is detailed in Esfahani et al [21,22].…”

Section: Experimental Designmentioning

confidence: 99%

“…An adaptive Kalman filter [40] was implemented in the SHARIF-HMIS system in order to reduce the error based on high-acceleration movement and to separate the linear acceleration variable from the gravity component [21,22]. In the current study, due to the lack of access to these programs and the limitations in the original clinical study that precluded the inclusion of added static postures needed for proper calibration [12,21,22], the output of the IMU sensors was directly used without adaptation. This also prevented the estimation of trunk angular position, as well as the compensation of the gravitational acceleration in the output of linear accelerometers.…”

Section: Limitationsmentioning

confidence: 99%

“…They concluded that simple flexion-extension movement in the sagittal plane, measured with only one sensor placed on the trunk, could distinguish healthy individuals from LBP patients with an accuracy of 96%, sensitivity of 100% and specificity of 92%. Esfahani et al [21,22] developed and validated the inertial sensors used in this study (SHARIF-HMIS). Repeatability and reliability of these sensors were investigated by Abedi et al [23].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Practical Sensor-Based Methodology for the Quantitative Assessment and Classification of Chronic Non Specific Low Back Patients (NSLBP) in Clinical Settings

Davoudi

Shokouhyan

Abedi

et al. 2020

Sensors

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The successful clinical application of patient-specific personalized medicine for the management of low back patients remains elusive. This study aimed to classify chronic nonspecific low back pain (NSLBP) patients using our previously developed and validated wearable inertial sensor (SHARIF-HMIS) for the assessment of trunk kinematic parameters. One hundred NSLBP patients consented to perform repetitive flexural movements in five different planes of motion (PLM): 0° in the sagittal plane, as well as 15° and 30° lateral rotation to the right and left, respectively. They were divided into three subgroups based on the STarT Back Screening Tool. The sensor was placed on the trunk of each patient. An ANOVA mixed model was conducted on the maximum and average angular velocity, linear acceleration and maximum jerk, respectively. The effect of the three-way interaction of Subgroup by direction by PLM on the mean trunk acceleration was significant. Subgrouping by STarT had no main effect on the kinematic indices in the sagittal plane, although significant effects were observed in the asymmetric directions. A significant difference was also identified during pre-rotation in the transverse plane, where the velocity and acceleration decreased while the jerk increased with increasing asymmetry. The acceleration during trunk flexion was significantly higher than that during extension, in contrast to the velocity, which was higher in extension. A Linear Discriminant Analysis, utilized for classification purposes, demonstrated that 51% of the total performance classifying the three STarT subgroups (65% for high risk) occurred at a position of 15° of rotation to the right during extension. Greater discrimination (67%) was obtained in the classification of the high risk vs. low-medium risk. This study provided a smart “sensor-based” practical methodology for quantitatively assessing and classifying NSLBP patients in clinical settings. The outcomes may also be utilized by leveraging cost-effective inertial sensors, already available in today’s smartphones, as objective tools for various health applications towards personalized precision medicine.

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Section: Experimental Designmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Practical Sensor-Based Methodology for the Quantitative Assessment and Classification of Chronic Non Specific Low Back Patients (NSLBP) in Clinical Settings

Davoudi

Shokouhyan

Abedi

et al. 2020

Sensors

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“…Unwearable systems are mainly used in laboratories as they typically require external emitters or cameras. However, wearable systems are portable and can be used in a variety of indoor and outdoor biomedical applications such as fall detection, rehabilitation, ergonomics, and sports biomechanics [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…IMU, an electronic device based on microelectromechanical systems (MEMS), consists of an accelerometer, a gyroscope, and an optional magnetometer [ 9 , 13 , 14 , 15 , 16 ]. It can be placed on body segments of interest, and capture segment kinematics (e.g., 3D acceleration, angular velocity, and angular orientations) in a body-fixed reference frame.…”

Section: Introductionmentioning

confidence: 99%

Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach

Esfahani

Zobeiri

Moshiri

et al. 2017

Sensors

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Human movement analysis is an important part of biomechanics and rehabilitation, for which many measurement systems are introduced. Among these, wearable devices have substantial biomedical applications, primarily since they can be implemented both in indoor and outdoor applications. In this study, a Trunk Motion System (TMS) using printed Body-Worn Sensors (BWS) is designed and developed. TMS can measure three-dimensional (3D) trunk motions, is lightweight, and is a portable and non-invasive system. After the recognition of sensor locations, twelve BWSs were printed on stretchable clothing with the purpose of measuring the 3D trunk movements. To integrate BWSs data, a neural network data fusion algorithm was used. The outcome of this algorithm along with the actual 3D anatomical movements (obtained by Qualisys system) were used to calibrate the TMS. Three healthy participants with different physical characteristics participated in the calibration tests. Seven different tasks (each repeated three times) were performed, involving five planar, and two multiplanar movements. Results showed that the accuracy of TMS system was less than 1.0°, 0.8°, 0.6°, 0.8°, 0.9°, and 1.3° for flexion/extension, left/right lateral bending, left/right axial rotation, and multi-planar motions, respectively. In addition, the accuracy of TMS for the identified movement was less than 2.7°. TMS, developed to monitor and measure the trunk orientations, can have diverse applications in clinical, biomechanical, and ergonomic studies to prevent musculoskeletal injuries, and to determine the impact of interventions.

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Smart textiles in healthcare: a summary of history, types, applications, challenges, and future trends

Esfahani¹

2021

Nanosensors and Nanodevices for Smart Multifunctional Textiles

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Sharif-human movement instrumentation system (SHARIF-HMIS) for daily activities

Cited by 7 publications

References 3 publications

A Practical Sensor-Based Methodology for the Quantitative Assessment and Classification of Chronic Non Specific Low Back Patients (NSLBP) in Clinical Settings

A Practical Sensor-Based Methodology for the Quantitative Assessment and Classification of Chronic Non Specific Low Back Patients (NSLBP) in Clinical Settings

Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach

Smart textiles in healthcare: a summary of history, types, applications, challenges, and future trends

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