A semi-autonomous motorized mobile hospital bed for safe transportation of head injury patients in dynamic hospital environments without bed switching

Application of a Machine Learning Algorithms in a Wrist-Wearable Sensor for Patient Health Monitoring during Autonomous Hospital Bed Transport

Smart sensors, coupled with artificial intelligence (AI)-enabled remote automated monitoring (RAMs), can free a nurse from the task of in-person patient monitoring during the transportation process of patients between different wards in hospital settings. Automation of hospital beds using advanced robotics and sensors has been a growing trend exacerbated by the COVID crisis. In this exploratory study, a polynomial regression (PR) machine learning (ML) RAM algorithm based on a Dreyfusian descriptor for immediate wellbeing monitoring was proposed for the autonomous hospital bed transport (AHBT) application. This method was preferred over several other AI algorithm for its simplicity and quick computation. The algorithm quantified historical data using supervised photoplethysmography (PPG) data for 5 min just before the start of the autonomous journey, referred as pre-journey (PJ) dataset. During the transport process, the algorithm continued to quantify immediate measurements using non-overlapping sets of 30 PPG waveforms, referred as in-journey (IJ) dataset. In combination, this algorithm provided a binary decision condition that determined if AHBT should continue its journey to destination by checking the degree of polynomial (DoP) between PJ and IJ. Wrist PPG was used as algorithm’s monitoring parameter. PPG data was collected simultaneously from both wrists of 35 subjects, aged 21 and above in postures mimicking that in AHBT and were given full freedom of upper limb and wrist movement. It was observed that the top goodness-of-fit which indicated potentials for high data accountability had 0.2 to 0.6 cross validation score mean (CVSM) occurring at 8th to 10th DoP for PJ datasets and 0.967 to 0.994 CVSM at 9th to 10th DoP for IJ datasets. CVSM was a reliable metric to pick out the best PJ and IJ DoPs. Central tendency analysis showed that coinciding DoP distributions between PJ and IJ datasets, peaking at 8th DoP, was the precursor to high algorithm stability. Mean algorithm efficacy was 0.20 as our proposed algorithm was able to pick out all signals from a conscious subject having full freedom of movement. This efficacy was acceptable as a first ML proof of concept for AHBT. There was no observable difference between subjects’ left and right wrists.

A fuzzy logic navigation controller implemented in hardware for an electric wheelchair

In this article, we present an obstacle avoidance controller implemented in a field programmable gate array for an electric wheelchair. It is based on a traditional approach with ultrasonic sensors and fuzzy logic. Various tests were conducted to characterize the prototype and to evaluate the controller performance. The results showed that the system is able to acquire data from sensors and make decisions 46.16 times per second. The sensors’ coverage extends 3 m to the front, rear, left, and right sides of the wheelchair; moreover, the sensors detect 0.95-cm diameter objects at 40 cm. The power consumption was evaluated, and it was found that the hardware architecture reduces the battery life by only 0.87%. Furthermore, the controller helped to navigate in confined areas, avoiding obstacles with cautious movements and decreasing the likelihood of collision. The proposed methodology uses data from eight sonars distributed around the wheelchair to make navigation decisions, besides the hardware-based architecture guarantees real-time control and on-time response.

A safe area search and map building algorithm for a wheeled mobile robot in complex unknown cluttered environments

In this paper, a safe map building and area search algorithm for a mobile robot in a closed unknown environment with obstacles is presented. A range finder sensor is used to detect the environment. The objective is to perform a complete search of the environment and build a complete map of it while avoiding collision with the obstacles. The developed robot navigation algorithm is randomized. It is proved that with probability 1 the robot completes its task in a finite time. Computer simulations and experiments with a real Pioneer-3DX robot confirm the performance of the proposed method.