Ergonomics has gained attention and take into consideration by the workers in the different fields of works recently. It has given a huge impact on the workers comfort which directly affects the work efficiency and productivity. The workers have claimed to suffer from the painful postures and injuries in their workplace. Musculoskeletal disorders (MSDs) is the most common problem frequently reported by the workers. This problem occurs due to the lack of knowledge and alertness from the workers to the ergonomic in their surroundings. This paper intends to review the approaches and instruments used by the previous works of the researchers in the evaluation of the ergonomics. The two main assessment methods often used for ergonomic evaluation are Rapid Upper Limb Assessment (RULA) and Rapid Entire Body Assessment (REBA). Popular devices are Inertial Measurement Units (IMU) and Microsoft Kinect.

This paper presents a rule-based real-time gait event/phase detection system (R-GEDS) using a shank mounted inertial measurement unit (IMU) for lower limb amputees during the level ground walking. Development of the algorithm is based on the shank angular velocity in the sagittal plane and linear acceleration signal in the shank longitudinal direction. System performance was evaluated with four control subjects (CS) and one transfemoral amputee (TFA) and the results were validated with four FlexiForce footswitches (FSW). The results showed a data latency for initial contact (IC) and toe off (TO) within a range of ± 40 ms for both CS and TFA. A delay of about 3.7 ± 62 ms for a foot-flat start (FFS) and an early detection of -9.4 ± 66 ms for heel-off (HO) was found for CS. Prosthetic side showed an early detection of -105 ± 95 ms for FFS whereas intact side showed a delay of 141 ±73 ms for HO. The difference in the kinematics of the TFA and CS is one of the potential reasons for high variations in the time difference. Overall, detection accuracy was 99.78% for all the events in both groups. Based on the validated results, the proposed system can be used to accurately detect the temporal gait events in real-time that leads to the detection of gait phase system and therefore, can be utilized in gait analysis applications and the control of lower limb prostheses.

Continuum robots are complex structures that require sophisticated modeling and control methods to achieve accurate position and motion tracking along desired trajectories. They are highly coupled, nonlinear systems with multiple degrees of freedom that pose a significant challenge for conventional approaches. In this paper, we propose a system dynamic model based on the Euler–Lagrange formulation with the assumption of piecewise constant curvature (PCC), where we accounts for the elasticity and gravity effects of the continuum robot. We also develop and apply a particle swarm optimization (PSO) algorithm to optimize the parameters of our developed controllers: an inverse dynamic proportional integral derivative (PID) controller and an inverse dynamic fuzzy logic controller (FLC), where we use the integral time of absolute error (ITAE) as the objective function for the PSO algorithm. We validate our proposed model and optimized controllers through different designed trajectories, simulated using our developed unique animated MATLAB simulation. The results show that the PSO-PID controller improves the rise time, overshoot percentage, and settling time by 16.3%, 31.1%, and 64.9%, respectively, compared to the PID controller without PSO. The PSO-FLC controller shows the best performance among all controllers, with a settling time of 0.7 s and a rise time of 0.4 s, leading to the highest level of precision in trajectory tracking. The ITAE error for the PSO-FLC controller is 11.4% and 29.9% lower than that of the PSO-PID and FLC controllers, respectively.

A motion capture system was constructed with 7- Inertial Measurement Units (IMU) covering the lower limb segments, with a wireless connection to a PC for data logging. IMU units were used to measure the lower limb segment's angular velocity and linear acceleration as direct biomechanical variables. Meanwhile, Lower Limb joints and segments inclination, in the sagittal plane, were calculated indirectly, using IMUs' direct outputs. The outputs undergo several signal conditioning phases to remove noises and bias. Furthermore, misalignment effect on the raw data is removed by an on-body calibration of the sensors. Finally, for activity recognition purposes, T-test is used to prove that capturing motion along only one side of the lower limb is sufficient, due to redundancy in the corresponding features of the biomechanical signals (variables) of each leg.