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About

Johan Eklund is a versatile researcher whose work spans biomedical signal processing, autonomous robotics, and model-based software engineering. He is perhaps best known for his pioneering contributions to human-machine interaction, particularly his development of EMG-based force estimation techniques. His highly cited 2007 paper on estimating elbow-induced wrist force using Fast Orthogonal Search (69 citations) demonstrated that inexpensive, portable EMG electrodes could reliably predict hand force—a breakthrough with direct implications for prosthetic arm control and human-robot interaction. This work built on his earlier 2005 investigations into the same methodology, establishing FOS as a powerful tool for biomechanical modeling. Eklund also made significant strides in autonomous robotics, developing nonlinear model predictive control algorithms for omnidirectional robot motion planning and real-time obstacle avoidance, work that has garnered over 30 citations and remains relevant to modern robotics research. His 2009 contribution examining model-based design through the lens of the DARPA Urban Challenge (22 citations) further demonstrated his breadth, offering practical insights into applying advanced software engineering methodologies to complex autonomous systems. Collectively, Eklund's research reflects a career devoted to bridging theoretical control and signal processing frameworks with real-world engineering challenges.