This study introduces a new EMG-based muscle fatigue index that combines EMG signal features in spatial, temporal, and spectral domains. This index incorporates a novel spatial EMG feature: Eigen Ratio Based Fatigue Index (ERFI) that can capture the variations in the active motor unit distribution during dynamic muscle fatiguing exercises. The new fatigue index maps the ERFI, a wavelet-based feature, a spectral feature, and an amplitude-based feature to the reduction in maximum voluntary contraction, which is considered the direct measure of muscle fatigue. The mapping function was implemented as a Multi-layer Perceptron (MLP). To evaluate the fatigue index, several fatigue tests under various speed and load conditions were conducted on four subjects. ERFI showed a significant variation (p < 0.01) over time for more than 85 % of the tests. Several MLP input configurations to predict muscle fatigue were compared in this study based on various combinations of EMG features. An input configuration that used the novel spatial EMG feature among five other features performed the best and was able to predict muscle fatigue with a mean coefficient of determination over 65 %. It was also noticed that ERFI’s relationship with muscle fatigue is less dependent on the load and speed of the cyclic exercise when compared with other EMG features that were proposed in previous studies. Thus it can be considered a better alternative to use in EMG-based control of active prosthetic and orthotic devices to compensate for the effect of muscle fatigue.

An intelligent wheelchair is a system that evolves its functions for the well-being of humankind in the present status of robotics. Due to the active evolution of these wheelchair systems wheelchair-mounted robotic arms can be caught as the next level of development. However, the current state of intelligence is far behind the expansion and this lack causes to add extra cognitive load on handicapped users. Most of the available hindrances to autonomous operation could certainly be achieved by replicating natural human behaviors. Under these circumstances understanding human cognition on positioning a wheelchair around a table will drastically ease the autonomous object manipulation tasks by these systems. Hence to understand natural human behavior, a human study with three sub-studies was designed and reveal the prominent factors behind human cognition. Results were analyzed statistically to identify the significance of the considered factors. In lite of the study, approaching and positioning of the wheelchair mainly depend on the object position, obstacle position, and obstacles configuration within the considered workspace. Further orientation mainly depends on the object's position and approaching direction. Besides, these outcomes would be extremely beneficial in synthesizing human cognition to build human-friendly mobile robots for uplifting users' lives as well.

Itis a prevalent trend nowadays in most places that the elderly population keeps increasing. Also, there is a community that has physical disabilities, especially mobility. Due to the lack of trustworthy caretakers, the busy lives of family members, and psychological issues attributed to loneliness, intelligent service robotic devices have been developed. Vocal navigational commands consist of uncertain terms. There are methods introduced to enhance these uncertainties using spatial parameters such as obstacle distances, previous robot movements, robot localization, etc. However, they have only considered the vocal information provided by the user and deduced the rest of the information from external spatial information. In reality, users include other partial information from complementary modalities such as hand gestures and there is a significant probability that the partial information carried by the hand gestures might change the interpretation completely. Therefore, this paper presents an intelligent system that would enhance the understanding of distance-related uncertainties of vocal navigational commands using multimodal fusion of hand gesture information. Partial gesture information extracted from hand gestures has been used to interpret the distance-related uncertainties using a fuzzy logic-based approach. Experiments were conducted to validate the intelligent system and the user ratings given were used to validate the system.

Many work-related tasks demand able-bodied employees to assume uncomfortable postures for long periods. This can lead to work-related musculoskeletal disorders. The situation demands the employees to be physically empowered while ensuring their agility and flexibility. In that context, wearable bodyweight support systems have come to the forefront as a futuristic solution to reduce the stress on the human body when assuming sitting and/or crouched postures. Recently, heightened attention on these posture assistive devices was evident with an increasing number of products, patents, and research articles. However, reviews of such wearable posture assistive devices for the lower limbs are lacking in the literature. Thus, this article specifically focuses on reviewing existing methods and technologies to assist workers in the industrial or service sectors to work in uncomfortable or crouched postures. Systematic reviews and meta-analyses extension for scoping reviews method was used to identify and analyze 43 unique designs from research and patent databases. The critical review of the technologies was performed after classifying these devices into commercial phase, lab/research phase, and conceptual design phase. This review has comprehensively covered the trends and patterns of the modern developments in posture assistance and includes future directions for the research community. Holistically, this article serves as a reference to set up a benchmarking system for wearable posture assistive devices.

Meal assistance robots provide disabled individuals the access to one of the important activities in daily living, self-feeding. This paper proposes a Steady State Visually Evoked Potential (SSVEP) based Brain Machine Interface (BMI) for controlling of a meal assistance robot. In the proposed system, the user has the facility to select any solid food item that he would like to eat from 3 different bowls just by looking at the respective LED matrices blinking at different frequencies. The generated SSVEP signals while looking at the LEDs are extracted from EEG signals acquired using OpenBCI EEG signal acquisition system. Extracted SSVEP signals are used to identify the intention of the user and subsequently the detected intentions are used to operate the meal assistant robot. Experiments are carried out to validate the system and results indicate the effectiveness of the proposed method.

This paper proposes an electromyography (EMG) signal based control method for a seven degrees of freedom (7DOF) upper-limb motion assist exoskeleton robot (SUEFUL-7). The SUEFUL-7 is able to assist the motions of shoulder vertical and horizontal flexion/extension, shoulder internal/external rotation, elbow flexion/extension, forearm supination/pronation, wrist flexion/extension, and wrist radial/ulnar deviation of physically weak individuals. In the proposed control method, an impedance controller is applied to the muscle-model-oriented control method by considering the end effector force vector. Impedance parameters are adjusted in real time by considering the upper-limb posture and EMG activity levels. Experiments have been performed to evaluate the effectiveness of the proposed robotic system.

We have been developing exoskeleton robots for assisting the motion of physically weak individuals such as elderly or slightly disabled in daily life. In this paper we propose an EMG-based control of a three degree of freedom (3-DOF) exoskeleton robot for the forearm pronation/supination motion, wrist flexion/extension motion and ulnar/radial deviation. The paper describes the hardware design of the exoskeleton robot and the control method. The skin surface electromyographic (EMG) signals of muscles in forearm of the exoskeleton's user and the hand force/forearm torque are used as input information for the proposed controller. By applying the skin surface EMG signals as main input signals to the controller, automatic control of the robot can be realized without manipulating any other equipment. Fuzzy control method has been applied to realize the natural and flexible motion assist. An experiment has been performed to evaluate the proposed exoskeleton robot.