An adaptive human sensor framework for human–robot collaboration

Manufacturing challenges are increasing the demands for more agile and dexterous means of production. At the same time, these systems aim to maintain or even increase productivity. The challenges risen from these developments can be tackled through human–robot collaboration (HRC). HRC requires effective task distribution according to each party’s distinctive strengths, which is envisioned to generate synergetic effects. To enable a seamless collaboration, the human and robot require a mutual awareness, which is challenging, due to the human and robot “speaking” different languages as in analogue and digital. This challenge can be addressed by equipping the robot with a model of the human. Despite a range of models being available, data-driven models of the human are still at an early stage. For this purpose, this paper proposes an adaptive human sensor framework, which incorporates objective, subjective, and physiological metrics, as well as associated machine learning. Thus, it is envisioned to adapt to the uniqueness and dynamic nature of human behavior. To test the framework, a validation experiment was performed, including 18 participants, which aims to predict perceived workload during two scenarios, namely a manual and an HRC assembly task. Perceived workloads are described to have a substantial impact on a human operator’s task performance. Throughout the experiment, physiological data from an electroencephalogram (EEG), an electrocardiogram (ECG), and respiration sensor was collected and interpreted. For subjective metrics, the standardized NASA Task Load Index was used. Objective metrics included task completion time and number of errors/assistance requests. Overall, the framework revealed a promising potential towards an adaptive behavior, which is ultimately envisioned to enable a more effective HRC.

An Adaptive Human Sensor Framework for Human-Robot Collaboration

Manufacturing challenges are increasing the demands for more agile and dexterous means of production. At the same time, these systems aim to maintain or even increase productivity. The challenges risen from these developments can be tackled through Human-Robot Collaboration (HRC). HRC requires effective task distribution according to each parties’ distinctive strengths, which is envisioned to generate synergetic effects. To enable a seamless collaboration, the human and robot require a mutual awareness, which is challenging, due to the human and robot “speaking” different languages as in analogue and digital. Thus, this challenge can be addressed by equipping the robot with a model of the human. Despite a range of models being available, data-driven models of the human are still at an early stage. This paper proposes an adaptive human sensor framework, which incorporates objective, subjective, and physiological metrics, as well as associated Machine Learning. Thus, it is envisioned to adapt to the uniqueness and dynamic nature of human behavior. To test the framework, a validation experiment was performed, including 18 participants, which aims to predict Perceived Workload during two scenarios, namely a manual and an HRC assembly task. Perceived Workloads are described to have a substantial impact on a human operator’s task performance. Throughout the experiment physiological data from an electroencephalogram (EEG), an electrocardiogram (ECG), and respiration sensor was collected and interpreted. For subjective metrics, the standardized NASA Task Load Index was used. Objective metrics included task completion time and number of errors/assistance requests. Overall, the framework revealed a promising potential towards an adaptive behavior, which is ultimately envisioned to enable a more effective HRC.

Respiratory Depression Following Cesarean Delivery With Single-Shot Spinal With 100 μg Morphine

Purpose: We aimed to evaluate the rate of cumulative bradypnea time (total bradypnea time/total monitoring time) and its related factors in these parturients.Methods: This was a prospective observational study of women undergoing elective and non-elective cesarean delivery under single-shot spinal including 0.1 mg morphine. The Berlin Questionnaire was used to screen for sleep apnea syndrome preoperatively. Respiratory rate and oxygen saturation (SpO2) were monitored continuously using an adhesive acoustic respiration sensor and pulse oximeter, respectively, at least 6 hours after cesarean delivery. Bradypnea was defined as a respiratory rate < 8 bpm lasting at least 25 s (sustained bradypnea) and at least 15 s (immediate bradypnea), respectively. Hypoxemia was defined as SpO2 < 92% lasting at least 25 s (sustained hypoxemia) and at least 15 s (immediate hypoxemia) multiple regression analysis was applied to assess related factors to the rate of cumulative sustained bradypnea.Results: Of 159 patients with a mean body mass index of 26.0 kg/m2, the Berlin Questionnaire was positive in 16.3%, and 77 (48.4%) experienced sustained bradypnea. The median rate of cumulative sustained bradypnea time was 0.70 % (interquartile range 0.35 to 1.45) without any related factors. The incidence of immediate bradypnea and sustained and immediate hypoxemia were 58.5%, 24.5%, and 37.7%, respectively. However, none of the factors were statistically significant. Conclusion: After cesarean delivery performed with intrathecal morphine 0.1 mg, respiratory depression events were commonly observed. However, the rate of cumulative bradypnea time was very low and there were no related factors.Trial registration number and date of registration: UMIN Clinical Trials Registry (UMIN 0035832) and Dec. 24th, 2018

A Battery-Less Wireless Respiratory Sensor Using Micro-Machined Thin-Film Piezoelectric Resonators

In this work, we present a battery-less wireless Micro-Electro-Mechanical (MEMS)-based respiration sensor capable of measuring the respiration profile of a human subject from up to 2 m distance from the transceiver unit for a mean excitation power of 80 µW and a measured SNR of 124.8 dB at 0.5 m measurement distance. The sensor with a footprint of ~10 cm2 is designed to be inexpensive, maximize user mobility, and cater to applications where disposability is desirable to minimize the sanitation burden. The sensing system is composed of a custom UHF RFID antenna, a low-loss piezoelectric MEMS resonator with two modes within the frequency range of interest, and a base transceiver unit. The difference in temperature and moisture content of inhaled and exhaled air modulates the resonance frequency of the MEMS resonator which in turn is used to monitor respiration. To detect changes in the resonance frequency of the MEMS devices, the sensor is excited by a pulsed sinusoidal signal received through an external antenna directly coupled to the device. The signal reflected from the device through the antenna is then analyzed via Fast Fourier Transform (FFT) to extract and monitor the resonance frequency of the resonator. By tracking the resonance frequency over time, the respiration profile of a patient is tracked. A compensation method for the removal of motion-induced artifacts and drift is proposed and implemented using the difference in the resonance frequency of two resonance modes of the same resonator.