Sharif-Human movement instrumentation system (SHARIF-HMIS): Development and validation

Esfahani, Mohammad Iman Mokhlespour; Akbari, Ali; Zobeiri, Omid A.; Rashedi, Ehsan; Parnianpour, Mohamad

doi:10.1016/j.medengphy.2018.07.008

Cited by 10 publications

(8 citation statements)

References 26 publications

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“…An inertial sensor (9DOF SHARIF-HMIS [21]), enclosing a 3-axis accelerometer (Analog Devices Inc., Norwood, MA, USA), a 3-axis gyroscope (InvenSense Inc., San Jose, CA, USA) and a 3-axis magnetometer (Honeywell Inc., Morris Plains, NJ, USA) with a 100 Hz frequency, was mounted vertically on the Xiphoid process of all participants. The captured data was transmitted to a PC-based data logger using an AVR microcontroller (Atmel Corp., San Jose, CA, USA).…”

Section: Experimental Designmentioning

confidence: 99%

“…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%

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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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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: 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

Self Cite

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show abstract

“…Among these, some have used wearable devices for measuring kinematic and kinetic parameters of human gait. For example, the inertial measurement unit (IMU) systems have gained attention for gait analysis, since the sensors are small, light and portable, thus are minimally intrusive for a range of applications [3,4]. Recently, few researchers have used similar technologies to quantify human muscle fatigue.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Model to Identify Fatigue Level Based on the Motion Data from a Smartphone

Karvekar

Abdollahi

Rashedi

2019

Preprint

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The fatigue due to repetitive and physically challenging jobs may result in workers' poor performance and Work-related Musculoskeletal Disorder (WMSD). Thus, it is imperative to frequently monitor fatigue and take necessary recovery actions. Our purpose was to develop a methodology to objectively classify subjects' fatigue level in the workplace utilizing the motion sensors embedded in the smartphones. An experiment consisting of twenty-four participants (12 M, 12 F) with a smartphone attached to their right shank was conducted using a fatiguing exercise (squatting), targeted mainly the lower extremity musculature. After each set of an exercise (2-min squatting), participants were asked about their ratings of perceived exertion (RPE), then a reference gait data were collected during a straight walk of 20-32 steps. This process was continued until they reported strong fatigue (≥17). Using the RPE to label the gait data, we have developed machine learning algorithms (i.e., binary and multi-class SVM models) to classify the individuals' gait into two (no-vs. strong-fatigue) and four levels (no-, low-, medium-, and strong-fatigue). The models reached the accuracies of 91% and 61% for two and four-level classification, respectively. The outcomes of this study may facilitate the implementation of a proactive approach in continuous monitoring of operators' fatigue level, which may subsequently increase the workers' performance and reduce the risk of WMSDs.

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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): Development and validation

Cited by 10 publications

References 26 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

A Data-Driven Model to Identify Fatigue Level Based on the Motion Data from a Smartphone

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

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