Background: The muscles that sustain body posture and the neck posture both have an impact on the cervical muscle, which is also frequently injured. The upper trapezius muscle is most frequently affected by trigger points, which is a common and prevalent condition. Aim: To assess the effectiveness of ELDOA exercises at the cervical spine and treating trigger points in the trapezius and levator scapulae. Methodology: The Ibn-e-Siena Hospital and Research Institute in Multan conducted a quasi-study. The participants were divided into two groups using a coin flip as a sampling method with the study sample size of 26. The Goniometer, Numeric Pain Rating Scale and Neck Disability Index were used to collect data from patients between the ages of 18 and 40. Results: An independent t-test was applied. Mean age ranged from 24.70±5.75 in the experiment group and 25.18±5.61 in the control group. The patients have radiated pain in control group was 3 (30%), trapezius trigger point7(70%) andlevator scapulae 3(30%) while in experimental group, pain radiating 6(60%), trapezius trigger point6(60%) and levator scapulae 4(40%). The post-results data revealed that the p values for the NPRS, Algometry and NDI significant differences were 0.025, 0.025, and 0.00, respectively. Conclusion: The results of the current investigation, the ELDOA approach considerably reduced discomfort, cervical ranges, and neck impairment brought on by trigger points. Keyword: Neck pain, Trigger Point, Skeletal Muscle, Myofascial Pain, Active Soft Tissue Release,

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Typically, humans interact with a humanoid robot with apprehension. This lack of trust can seriously affect the effectiveness of a team of robots and humans. We can create effective interactions that generate trust by augmenting robots with an explanation capability. The explanations provide justification and transparency to the robot’s decisions. To demonstrate such effective interaction, we tested this with an interactive, game-playing environment with partial information that requires team collaboration, using a game called Spanish Domino. We partner a robot with a human to form a pair, and this team opposes a team of two humans. We performed a user study with sixty-three human participants in different settings, investigating the effect of the robot’s explanations on the humans’ trust and perception of the robot’s behaviour. Our explanation-generation mechanism produces natural-language sentences that translate the decision taken by the robot into human-understandable terms. We video-recorded all interactions to analyse factors such as the participants’ relational behaviours with the robot, and we also used questionnaires to measure the participants’ explicit trust in the robot. Overall, our main results demonstrate that explanations enhanced the participants’ understandability of the robot’s decisions, because we observed a significant increase in the participants’ level of trust in their robotic partner. These results suggest that explanations, stating the reason(s) for a decision, combined with the transparency of the decision-making process, facilitate collaborative human–humanoid interactions.