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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.

A new wearable assistive device (WAD) was developed to decrease required force on the lumbar spine in static holding tasks. In order to obtain moments on lumbar spine in two conditions, with and without WAD, a biomechanical static model was used for estimation of external moments on lumbar spine. The results of biomechanical models indicated that there was a reduction in the lumbar moment ranging from 20% to 43% using WAD depending on the load and flexion angle. A total of 15 male healthy subjects were tested to experimentally verify the predicted reduction of external moments on the spine by wearing WAD. Normalized electromyography (EMG) of the right and left lumbar and thoracic erector spinae (LES, TES), latissimus dorsi (LD), external oblique (EO), internal oblique (IO) and rectus abdominus (RA) muscles were monitored at three lumbar flexion positions (0°, 30° and 60°) in symmetric posture with three different loads (0, 5 and 15 kg) in two conditions of with and without WAD. The effects of WAD and load were significant for all muscles but the interaction effects were only significant for extensor muscles groups (p < 0.016). Results of statistical analysis (ANOVA) on the normalized EMG while wearing WAD indicated that the muscle activity of right and left LES, TES and LD muscles significantly decreased (p < 0.001). This reduction for right LES, TES, LD muscles at 15 kg load and 60° trunk flexion were 23.2%, 30% and 27.8%, respectively which were in good agreement with the biomechanical model results.

The biomechanical behavior of brain tissue is needed for predicting the traumatic brain injury (TBI). Each year over 1.5 million people sustain a TBI in the United States. The appropriate coefficients for modeling the injury prediction can be evaluated using experimental data. In the present paper, using an experimental setup on bovine brain tissue, unconfined compression tests at quasi-static strain rates of ε. = 0.0004s−1, 0.008s−1 and 0.4s−1 combined with a stress relaxation test under unconfined uniaxial compression with ε. = 0.67s−1 ramp rate are performed. The fitted visco-hyperelastic parameters were utilized by using obtained stress-strain curves. The finite element analysis (FEA) is validated using experimental data.

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Central nervous system (CNS) uses vision, vestibular, and somatosensory information to maintain body stability. Research has shown that there is more lumbar proprioception error among low back pain (LBP) individuals as compared to healthy people. In this study, two groups of 20 healthy people and 20 non-specific low back pain (NSLBP) participants took part in this investigation. This investigation focused on somatosensory sensors and in order to alter proprioception, a vibrator (frequency of 70 Hz, amplitude of 0.5 mm) was placed on the soleus muscle area of each leg and two vibrators were placed bilaterally across the lower back muscles. Individuals, whose vision was occluded, were placed on two surfaces (foam and rigid) on force plate, and trunk angles were recorded simultaneously. Tests were performed in eight separate trials; the independent variables were vibration (four levels) and surface (two levels) for within subjects and two groups (healthy and LBP) for between subjects (4 × 2 × 2). MANOVA and multi-factor ANOVA tests were done. Linear parameters for center of pressure (COP) [deviation of amplitude, deviation of velocity, phase plane portrait (PPP), and overall mean velocity] and non-linear parameters for COP and trunk angle [recurrence quantification analysis (RQA) and Lyapunov exponents] were chosen as dependent variables. Results indicated that NSLBP individuals relied more on ankle proprioception for postural stability. Similarly, RQA parameters for the COP on both sides and for the trunk sagittal angle indicated more repeated patterns of movement among the LBP cohort. Analysis of short and long Lyapunov exponents showed that people with LBP caused no use of all joints in their bodies (non-flexible), are less stable than healthy subjects.

In this paper, a common energy harvester is investigated which uses a specimen of magnetic shape memory alloy (MSMA). The aim of this study is to improve system performance and to evaluate the magneto-mechanical loading on the MSMA material. Since demagnetization effect is not included in the employed original MSMA model, a method to incorporate this effect is proposed which has a good performance for the specific magneto-mechanical loading of this problem. In order to decrease the need for bias magnetic field and increase system efficiency, a new return mechanism for the MSMA specimen is proposed. The results indicate that the maximum harvested power from the improved system is obtained at 0.55 T bias field, with 30% increase in power. Then, input mechanical loading in the system is studied. Firstly, applied strain rate caused by mechanical loading is studied, and a nonlinear relation between the induced RMS voltage and strain rate is observed. Next, 2D applied mechanical loading is investigated, and it is shown that by increasing the phase difference between mechanical loads in two directions, the induced voltage decreases. Moreover, applying dynamic effect to the model shows that for thin MSMA specimens, this effect is minor, but by increasing the thickness and loading frequency, the effect becomes tangible.

The central nervous system (CNS) dynamically employs a sophisticated weighting strategy of sensory input, including vision, vestibular and proprioception signals, towards attaining optimal postural control during different conditions. Non-specific low back pain (NSLBP) patients frequently demonstrate postural control deficiencies which are generally attributed to challenges in proprioceptive reweighting, where they often rely on an ankle strategy regardless of postural conditions. Such impairment could lead to potential loss of balance, increased risk of falling, and Low back pain recurrence. In this study, linear and non-linear indicators were extracted from center-of-pressure (COP) and trunk sagittal angle data based on 4 conditions of vibration positioning (vibration on the back, ankle, none or both), 2 surface conditions (foam or rigid), and 2 different groups (healthy and non-specific low back pain patients). Linear discriminant analysis (LDA) was performed on linear and non-linear indicators to identify the best sensory condition towards accurate distinction of non-specific low back pain patients from healthy controls. Two indicators: Phase Plane Portrait ML and Entropy ML with foam surface condition and both ankle and back vibration on, were able to completely differentiate the non-specific low back pain groups. The proposed methodology can help clinicians quantitatively assess the sensory status of non-specific low back pain patients at the initial phase of diagnosis and throughout treatment. Although the results demonstrated the potential effectiveness of our approach in Low back pain patient distinction, a larger and more diverse population is required for comprehensive validation.