Browsing by Author "Mokhlespour Esfahani, Mohammad Iman"

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    Classifying Diverse Physical Activities Using “Smart Garments”
    Mokhlespour Esfahani, Mohammad Iman; Nussbaum, Maury A. (MDPI, 2019-07-16)
    Physical activities can have important impacts on human health. For example, a physically active lifestyle, which is one of the most important goals for overall health promotion, can diminish the risk for a range of physical disorders, as well as reducing health-related expenditures. Thus, a long-term goal is to detect different physical activities, and an important initial step toward this goal is the ability to classify such activities. A recent and promising technology to discriminate among diverse physical activities is the smart textile system (STS), which is becoming increasingly accepted as a low-cost activity monitoring tool for health promotion. Accordingly, our primary aim was to assess the feasibility and accuracy of using a novel STS to classify physical activities. Eleven participants completed a lab-based experiment to evaluate the accuracy of an STS that featured a smart undershirt (SUS) and commercially available smart socks (SSs) in discriminating several basic postures (sitting, standing, and lying down), as well as diverse activities requiring participants to walk and run at different speeds. We trained three classification methods—K-nearest neighbor, linear discriminant analysis, and artificial neural network—using data from each smart garment separately and in combination. Overall classification performance (global accuracy) was ~98%, which suggests that the STS was effective for discriminating diverse physical activities. We conclude that, overall, smart garments represent a promising area of research and a potential alternative for discriminating a range of physical activities, which can have positive implications for health promotion.
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    Development and Assessment of Smart Textile Systems for Human Activity Classification
    Mokhlespour Esfahani, Mohammad Iman (Virginia Tech, 2018-09-13)
    Wearable sensors and systems have become increasingly popular for diverse applications. An emerging technology for physical activity assessment is Smart Textile Systems (STSs), comprised of sensitive/actuating fiber, yarn, or fabric that can sense an external stimulus. All required components of an STS (sensors, electronics, energy supply, etc.) can be conveniently embedded into a garment, providing a fully textile-based system. Thus, STSs have clear potential utility for measuring health-relevant aspects of human activity, and to do so passively and continuously in diverse environments. For these reasons, STSs have received increasing interest in recent studies. Despite this, however, limited evidence exists to support the implementation of STSs during diverse applications. Our long-term goal was to assess the feasibility and accuracy of using an STS to monitor human activities. Our immediate objective was to investigate the accuracy of an STS in three representative applications with respect to occupational scenarios, healthcare, and activities of daily living. A particular STS was examined, consisting of a smart socks (SSs), using textile pressure sensors, and smart undershirt (SUS), using textile strain sensors. We also explored the relative merits of these two approaches, separately and in combination. Thus, five studies were completed to design and evaluate the usability of the smart undershirt, and investigate the accuracy of implementing an STS in the noted applications. Input from the SUS led to planar angle estimations with errors on the order of 1.3 and 9.4 degrees for the low-back and shoulder, respectively. Overall, individuals preferred wearing a smart textile system over an IMU system and indicated the former as superior in several aspects of usability. In particular, the short-sleeved T-shirt was the most preferred garments for an STS. Results also indicated that the smart shirt and smart socks, both individually and in combination, could detect occupational tasks, abnormal and normal gaits, and activities of daily living with greater than 97% accuracy. Based on our findings, we hope to facilitate future work that more effectively quantifies sedentary periods that may be deleterious to human health, as well as detect activity types that may be help or hinder health and fitness. Such information may be of use to individuals and workers, healthcare providers, and ergonomists. More specifically, further analyses from this investigation could provide strategies for: (a) modifying a sedentary lifestyle or work scenario to a more active one, and (b) helping to more accurately identify occupational injury risk factors associated with human movement.
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    Preferred Placement and Usability of a Smart Textile System vs. Inertial Measurement Units for Activity Monitoring
    Mokhlespour Esfahani, Mohammad Iman; Nussbaum, Maury A. (MDPI, 2018-08-01)
    Wearable sensors and systems have become increasingly popular in recent years. Two prominent wearable technologies for human activity monitoring are smart textile systems (STSs) and inertial measurement units (IMUs). Despite ongoing advances in both, the usability aspects of these devices require further investigation, especially to facilitate future use. In this study, 18 participants evaluate the preferred placement and usability of two STSs, along with a comparison to a commercial IMU system. These evaluations are completed after participants engaged in a range of activities (e.g., sitting, standing, walking, and running), during which they wear two representatives of smart textile systems: (1) a custom smart undershirt (SUS) and commercial smart socks; and (2) a commercial whole-body IMU system. We first analyze responses regarding the usability of the STS, and subsequently compared these results to those for the IMU system. Participants identify a short-sleeved shirt as their preferred activity monitor. In additional, the SUS in combination with the smart socks is rated superior to the IMU system in several aspects of usability. As reported herein, STSs show promise for future applications in human activity monitoring in terms of usability.
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    Recovering from Laboratory-Induced slips and trips causes high levels of lumbar muscle activity and spine loading
    Rashedi, Ehsan; Kathawala, Kavish; Abdollahi, Masoud; Alemi, Mohammad Mehdi; Mokhlespour Esfahani, Mohammad Iman; Nussbaum, Maury A. (Elsevier, 2023-02)
    Slips, trips, and falls are some of the most substantial and prevalent causes of occupational injuries and fatalities, and these events may contribute to low-back problems. We quantified lumbar kinematics (i.e., lumbar angles relative to pelvis) and kinetics during unexpected slip and trip perturbations, and during normal walking, among 12 participants (6F, 6M). Individual anthropometry, lumbar muscle geometry, and lumbar angles, along with electromyography from 14 lumbar muscles were used as input to a 3D, dynamic, EMG-based model of the lumbar spine. Results indicated that, in comparison with values during normal walking, lumbar range of motion, lumbosacral (L5/S1) loads, and lumbar muscle activations were all significantly higher during the slip and trip events. Maximum L5/S1 compression forces exceeded 2700 N during slip and trip events, compared with ~1100 N during normal walking. Mean values of L5/S1 anteroposterior (930 N), and lateral (800 N) shear forces were also substantially larger than the shear force during the normal walking (230 N). These observed levels of L5/S1 reaction forces, along with high levels of bilateral lumbar muscle activities, suggest the potential for overexertion injuries and tissue damage during unexpected slip and trip events, which could contribute to low back injuries. Outcomes of this study may facilitate the identification and control of specific mechanisms involved with low back disorders consequent to slips or trips.
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    Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach
    Mokhlespour Esfahani, Mohammad Iman; Zobeiri, Omid; Moshiri, Behzad; Narimani, Roya; Mehravar, Mohammad; Rashedi, Ehsan; Parnianpour, Moahmad (MDPI, 2017-01-08)
    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.