Evaluation of Biomechanical and Mental Workload During Human-Robot Collaborative Pollination Task.

OBJECTIVE: The purpose of this study is to identify the potential biomechanical and cognitive workload effects induced by human robot collaborative pollination task, how additional cues and reliability of the robot influence these effects and whether interacting with the robot influences the participant's anxiety and attitude towards robots. BACKGROUND: Human-Robot Collaboration (HRC) could be used to alleviate pollinator shortages and robot performance issues. However, the effects of HRC for this setting have not been investigated. METHODS: Sixteen participants were recruited. Four HRC modes, no cue, with cue, unreliable, and manual control were included. Three categories of dependent variables were measured: (1) spine kinematics (L5/S1, L1/T12, and T1/C7), (2) pupillary activation data, and (3) subjective measures such as perceived workload, robot-related anxiety, and negative attitudes towards robotics. RESULTS: HRC reduced anxiety towards the cobot, decreased joint angles and angular velocity for the L5/S1 and L1/T12 joints, and reduced pupil dilation, with the "with cue" mode producing the lowest values. However, unreliability was detrimental to these gains. In addition, HRC resulted in a higher flexion angle for the neck (i.e., T1/C7). CONCLUSION: HRC reduced the physical and mental workload during the simulated pollination task. Benefits of the additional cue were minimal compared to no cues. The increased joint angle in the neck and unreliability affecting lower and mid back joint angles and workload requires further investigation. APPLICATION: These findings could be used to inform design decisions for HRC frameworks for agricultural applications that are cognizant of the different effects induced by HRC.

Wearable Sensors Assess the Effects of Human-Robot Collaboration in Simulated Pollination.

Pollination for indoor agriculture is hampered by environmental conditions, requiring farmers to pollinate manually. This increases the musculoskeletal illness risk of workers. A potential solution involves Human-Robot Collaboration (HRC) using wearable sensor-based human motion tracking. However, the physical and biomechanical aspects of human interaction with an advanced and intelligent collaborative robot (cobot) during pollination remain unknown. This study explores the impact of HRC on upper body joint angles during pollination tasks and plant height. HRC generally resulted in a significant reduction in joint angles with flexion decreasing by an average of 32.6 degrees (p ≤ 0.001) for both shoulders and 30.5 degrees (p ≤ 0.001) for the elbows. In addition, shoulder rotation decreased by an average of 19.1 (p ≤ 0.001) degrees. However, HRC increased the left elbow supination by 28.3 degrees (p ≤ 0.001). The positive effects of HRC were reversed when the robot was unreliable (i.e., missed its target), but this effect was not applicable for the left elbow. The effect of plant height was limited with higher plant height increasing right shoulder rotation but decreasing right elbow pronation. These findings aim to shed light on both the benefits and challenges of HRC in agriculture, providing valuable insights before deploying cobots in indoor agricultural settings.

Human mobile robot interaction in the retail environment.

As technology advances, Human-Robot Interaction (HRI) is boosting overall system efficiency and productivity. However, allowing robots to be present closely with humans will inevitably put higher demands on precise human motion tracking and prediction. Datasets that contain both humans and robots operating in the shared space are receiving growing attention as they may facilitate a variety of robotics and human-systems research. Datasets that track HRI with rich information other than video images during daily activities are rarely seen. In this paper, we introduce a novel dataset that focuses on social navigation between humans and robots in a future-oriented Wholesale and Retail Trade (WRT) environment ( https://uf-retail-cobot-dataset.github.io/ ). Eight participants performed the tasks that are commonly undertaken by consumers and retail workers. More than 260 minutes of data were collected, including robot and human trajectories, human full-body motion capture, eye gaze directions, and other contextual information. Comprehensive descriptions of each category of data stream, as well as potential use cases are included. Furthermore, analysis with multiple data sources and future directions are discussed.

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review.

With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.

Evaluating the impact of writing surface and configuration on muscle activation level during a handwriting task: An exploratory study.

BACKGROUND: Tablets are ubiquitous in workplaces and schools. However, there have been limited studies investigating the effect tablets have on the body during digital writing activities. OBJECTIVE: This study investigated the biomechanical impact of writing interface design (paper, whiteboard, and tablet) and orientation (horizontal, 45°, and vertical) on tablet users. METHODS: Fourteen adults (7 male, 7 female) participated in a study during which they performed simple writing tasks. Surface electromyography (sEMG) sensors were used to measure upper extremity muscle activation. RESULTS: Results indicate that the effects of writing surface type were most pronounced in forearm muscle activation. Specifically, in the extensor carpi radialis (ECR), where muscle activity was lower on the tablet PC surface. The effects of writing configuration were prominent in the shoulder and forearm. The activation of the flexor carpi ulnaris (FCU) and trapezius muscles was significantly lower in the 45° configuration. An exception to the efficacy of this configuration was the anterior deltoid muscle, which exhibited the lowest muscle activity in the horizontal orientation. CONCLUSIONS: Tablet surface and the 45° configuration resulted in the lowest muscle activation levels. Future studies should include longer experiment duration to investigate the effects of continuous writing.

Load Carriage Modes and Limb Crossing Patterns Altered Gait during Obstacle Negotiation.

Personal and environmental factors both increase the likelihood of falling injuries while negotiating obstacles. Eighteen male participants (seven older, eleven young) were recruited to walk over an obstacle with and without loads on their hands to study the effects of age, load carriage modes, and limb crossing patterns on gait during obstacle negotiation. Participants initiated tasks with either their dominant or non-dominant leg. Step length (SL), toe clearance (TC), step velocity (V), and step width (SW) were extracted from four critical steps. Results showed that during obstacle negotiation (1) older adults had more TC than younger adults, (2) hand loads affected SL and TC, (3) gait parameters are dissimilar between the dominant limb and non-dominant limb.