Sit-to-stand (STS) is an important functional task affected by low back pain (LBP). It requires fundamental coordination among all segments of the body to control important performance variables such as body's center of mass (CM) and head positions. This study was conducted to determine whether LBPs could coordinate their multiple joints to achieve the task stability to the same extent as healthy controls. About 11 non-specific chronic LBP and 12 healthy control subjects performed STS task at three postural difficulty levels: rigid surface — open eyes (RO), rigid surface — closed eyes (RC) and narrow surface — closed eyes (NC). Motion variability of seven body segments, CM and head positions were calculated across 15 trials, and uncontrolled manifold (UCM) approach was used to investigate joint coordination. This approach partitioned segment angle variations into component that stabilizes a given performance variable and leads to task performance flexibility (UCM variability: V UCM ) and that which does not stabilize the performance variable and leads to task performance error (orthogonal variability: V ORT ). The results showed that LBPs demonstrated significantly less V UCM regarding the control of horizontal CM position and greater V ORT regarding the control of horizontal head position. The current findings revealed that multijoint coordination was impaired in the LBP subjects. These altered motor coordination strategies would make their postural control system less adaptive to altered postural demands and may predispose these subjects to re-injury.

Purpose The purpose of this study is the measuring of the human movement using printed wearable sensor. Human movement measurement is one of the usages for wearable sensors. This technology assists the researchers to collect data from the daily activities of individuals. In other words, the kinematics data of human motion will be extracted from this data and implemented in biomechanical aspects. Design/methodology/approach This study presents an innovative printed wearable sensor which can be used for measuring human movement orientations. In this paper, the manufacturing process, implementation, measurement setup and calibration procedure of this new sensor will be explained, and the results of calibration methods will be presented. The conductive flexible nylon/lycra fabric strain gauge was developed using polypyrrole (PPy)–1, 5-naphthalenedisulfonic acid by using a sophisticated method composed of screen printing followed by chemical vapor deposition at room temperature. Findings The morphological characterization using scanning electron microscopy shows the PPy-coated fabric exhibiting a homogenous and smooth surface. Based on the results, the linearity and hysteresis error are 98 and 8 per cent, respectively. Finally, the behavior of our sensor is evaluated in some cases, and the effects of relaxation and strain rate will be discussed. Practical implications The wearable sensor is one of the most advanced technologies in biomedical engineering. It can be used in several applications for prohibition, diagnosing and treatment of diseases. Originality/value The paper present original data acquired from a technical set-up in biomechanic labs. An innovative method was used for collecting the resistance changing of the sensor. A measurement setup was prepared as a transducer to convert the resistance into voltage.

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.

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.

Sit to stand (STS) task requires variability of all body segments to achieve the stability of the important control variables (i.e., center of mass (CM) and head positions). In this study, the possible differences in the variability patterns of various body segments were investigated between 11 chronic low back pain (LBP) and 12 control subjects during STS task through two types of variability analyses; first by calculating the variability of seven limb angles, CM and head positions across 15 trials and second by principal component analysis (PCA) of seven limb angles. Participants performed the task at 3 postural difficulty levels: rigid surface, open eyes (RO), rigid surface, close eyes (RC) and narrow surface, close eyes (NC). The results revealed that LBPs could stabilize the CM and head positions same as controls. Also there was more than 1 synergic combination of whole body segments in both LBP and healthy groups. But the number of PCs accounting for the major part of variance was reduced in the LBPs in the most unstable phase of movement (50%–80% trajectory) in the RO and RC conditions. This may indicate that LBPs have reduced flexibility in the most unstable phase of task.