Recent advancements in exoskeleton technology, both passive and active, are driven by the need to enhance human capabilities across various industries as well as the need to provide increased safety for the human worker. This review paper examines the sensors, actuators, mechanisms, design, and applications of passive and active exoskeletons, providing an in-depth analysis of various exoskeleton technologies. The main scope of this paper is to examine the recent developments in the exoskeleton developments and their applications in different fields and identify research opportunities in this field. The paper examines the exoskeletons used in various industries as well as research-level prototypes of both active and passive types. Further, it examines the commonly used sensors and actuators with their advantages and disadvantages applicable to different types of exoskeletons. Communication protocols used in different exoskeletons are also discussed with the challenges faced.

Electromyography (EMG) has emerged as a vital tool in the development of wearable robotic exoskeletons, enabling intuitive and responsive control by capturing neuromuscular signals. This review presents a comprehensive analysis of the EMG signal processing pipeline tailored to exoskeleton applications, spanning signal acquisition, noise mitigation, data preprocessing, feature extraction, and control strategies. Various EMG acquisition methods, including surface, intramuscular, and high-density surface EMG, are evaluated for their applicability in real-time control. The review addresses prevalent signal quality challenges, such as motion artifacts, power-line interference, and crosstalk. It also highlights both traditional filtering techniques and advanced methods, such as wavelet transforms, empirical mode decomposition, and adaptive filtering. Feature extraction techniques are explored to support pattern recognition and motion classification. Machine learning approaches are examined for their roles in pattern recognition-based and hybrid control architectures. This article emphasizes muscle synergy analysis and adaptive control algorithms to enhance personalization and fatigue compensation, followed by the benefits of multimodal sensing and edge computing in addressing the limitations of EMG-only systems. By focusing on EMG-driven strategies through signal processing, machine learning, and sensor fusion innovations, this review bridges gaps in human–machine interaction, offering insights into improving the precision, adaptability, and robustness of next generation exoskeletons.

Exoskeletons possess a high potential for assisting the human workforce while eliminating or reducing the risk of Work-Related Musculoskeletal Disorders (WMSDs). However, their usage in agricultural work, where there is a plethora of reported WMSD cases, seems limited. Since agricultural tasks are complex and performed in harsh environments, developing novel exoskeleton-based solutions could be challenging. However, commercial exoskeletons are already being used in various other industries, such as logistics, military, medicine, and manufacturing. Thus, it is expected that those existing exoskeleton solutions could be applied to agricultural tasks. Nevertheless, prior to implementation, assessing the feasibility, efficacy, and necessary modifications for these exoskeletons is imperative to supporting agricultural activities prone to WMSDs. In this review, prevalent exoskeletons documented in scientific literature are identified, and their potential relevance to agricultural tasks with elevated WMSD risks is evaluated. The review further highlights and deliberates on exoskeletons that could be applicable in an agricultural context. This comprehensive examination serves as a foundational step towards the conceptualization and development of exoskeleton-based approaches tailored explicitly for agricultural tasks.

Electromyography (EMG) has become essential in biomedical engineering, rehabilitation, and human–machine interfacing due to its ability to capture neuromuscular activation for control, monitoring, and diagnosis. Recent advances in sensing hardware, high-density and flexible electrodes, and embedded acquisition modules combined with modern signal processing and machine learning have significantly enhanced the robustness and applicability of EMG-based systems. This review provides an integrated overview of EMG generation, acquisition standards, and preprocessing techniques, including adaptive filtering, wavelet denoising, and empirical mode decomposition. Feature extraction methods across the time, frequency, time–frequency, and nonlinear domains are compared with respect to computational efficiency and suitability for real-time systems. The review synthesizes classical and contemporary pattern-recognition approaches, from statistical classifiers to deep architectures such as CNNs, RNNs, hybrid CNN–RNN models, transformer-based networks, and graph neural networks. Key challenges, including signal non-stationarity, electrode displacement, muscle fatigue, and poor cross-user or cross-session generalization, are examined alongside emerging strategies such as transfer learning, domain adaptation, and multimodal fusion with IMU or FMG signals. Finally, the paper surveys rapidly growing EMG applications in prosthetics, rehabilitation robotics, human–machine interfaces, clinical diagnostics, and sports analytics. The review highlights ongoing limitations and outlines future pathways toward robust, adaptive, and deployable EMG-driven intelligent systems.

Citrus harvesting involves repetitive lower-limb and postural activities that expose workers to elevated risks of work-related musculoskeletal disorders (WMSDs), particularly affecting the lower back and lower limbs. Passive lower-limb exoskeletons offer a potential low-complexity solution, which does not drastically change the current harvesting process, to mitigate these physical demands; however, their feasibility for agricultural applications remains insufficiently explored. This study presents the design and laboratory-based proof-of-concept evaluation of a prototype passive lower-limb exoskeleton intended to assist citrus harvesting tasks. The proposed system incorporates a mechanical load redistribution frame with passive elastic elements to support hip and knee joint movements during harvesting postures without requiring external power or active control. The design was informed mainly by literature, local industry consultation, and simplified biomechanical modeling approaches. The prototype’s feasibility was assessed under controlled laboratory conditions using simulated harvesting squat motions across a range of loading conditions. Preliminary observations indicate a reduction in musculoskeletal demand during supported tasks compared to conventional load-carrying conditions, suggesting that the proposed mechanism can partially offload effort of lower-limb muscles. Overall, this work presents a proof-of-concept prototype and feasibility assessment, offering preliminary baseline indications under single-subject laboratory conditions rather than generalizable evidence for passive lower-limb exoskeletons in harvesting activities.