Dynamic Balance Control and Postural Adaptation in Human-Robot Collaborative Manipulation: Within-Subject
Alessia de Nobile1, Daniele Bibbo1, Simone Ranaldi1
1Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Via Vito Volterra, 62 - Corpo B, Roma, 00146, Italy, 39 0657337298.
Background:
The integration of robots into industrial settings has rapidly advanced, aiming to reduce human involvement in demanding tasks while improving overall efficiency. As collaborative robots (cobots) become more prevalent, assessing the physical strain during joint tasks is essential to promote long-term well-being in the workplace.
Objective:
This study aimed to investigate how human-robot collaboration influences workers' postural control and musculoskeletal load during manipulation tasks performed in parallel.
Methods:
Fourteen healthy male participants performed manipulation tasks under 3 conditions: without robotic assistance, with a cobot providing load support (Robot Free [RF]) and a cobot constrained to horizontal movement (Robot Plane [RP]). Center of pressure trajectories were computed, and nonlinear recurrence quantification analysis indicators (recurrence rate [REC], determinism [DET], and their ratio) were calculated in the anteroposterior, mediolateral, and anteroposterior-mediolateral planes.
Results:
Statistical analysis showed greater postural sway in robot-assisted conditions compared to Free. Mean distance increased from 1.7 (SD 0.6) cm in Free to 2.4 (SD 0.6) cm in RF (P<.001) and 2.3 (SD 0.6) cm in RP (P<.001). Mean velocity increased from 2.9 (SD 0.9) cm/s in Free to 4.3 (SD 1.4) cm/s in RF and RP. Confidence ellipse area increased from 7.6 (SD 4.1) cm2 in Free to 24.9 (SD 14.2) cm2 in RF and 23.1 (SD 13.4) cm2 in RP. Sway area increased from 1.5 (SD 0.7) cm2/s in Free to 2.9 (SD 1.2) cm2/s in RF and RP. Nonlinear metrics revealed lower recurrence rates in robot-assisted conditions, decreasing from 0.31 (SD 0.08) in Free to 0.2 (SD 0.08) in RF and 0.2 (SD 0.04) in RP in the anteroposterior-mediolateral plane (P<.001), from 0.33 (SD 0.08) in Free to 0.28 (SD 0.07) in RF (P=.02) and 0.16 (SD 0.03) in RP (P=.007) in the mediolateral direction, and from 0.36 (SD 0.07) in Free to 0.3 (SD 0.06) in RF (P=.009) and 0.26 (SD 0.03) in RP (P<.001) in the anteroposterior direction. Determinism remained stable (values close to 1), leading to higher determinism-to-recurrence ratios for robot-assisted conditions, increasing from 3.41 (SD 0.87) in Free to 5.41 (SD 1.69) in RF and 5.51 (SD 1.11) in RP in the anteroposterior-mediolateral plane (P<.001), from 3.11 (SD 0.63) in Free to 3.64 (SD 0.72) in RF (P=.02) and 3.92 (SD 0.48) in RP (P=.007) in the mediolateral direction, and from 2.82 (SD 0.48) in Free to 3.47 (SD 0.57) in RF (P=.009) and 3.46 (SD 0.45) in RP (P<.001) in the anteroposterior direction. No significant differences were found between the robot-assisted conditions.
Conclusions:
Interaction increases postural sway, indicating reduced stability and higher physical demand. This could reflect impaired balance or adaptation. Nonlinear analysis reveals that postural control remains structured. Results also suggest that the mere presence of the cobot is the primary driver of these postural changes.
