Layered Path Planning with Human Motion Detection for Autonomous Robots

Reactively planning a path in a dynamic and unstructured environment is a key challenge for mobile robots and autonomous systems. Planning should consider factors including the long-term and short-term prediction, current environmental situation, and human context. In this chapter, we present a novel robotic path-planning method with human activity information in a large-scale three-dimensional (3D) environment. In the learning stage, this method uses human motion detection results and preliminary environmental information to build a long-term context model with a hidden Markov model (HMM) to describe and predict human activities in the environment. In the application stage, when a robot detects humans in the environment, it first uses the long-term context model to generate impedance areas in the environment. Then, the robot searches each area of the environment to find paths between key locations, such as escalators, to generate a Reactive Key Cost Map (RKCM), whose vertexes are those key locations and edges are generated paths. The graphs of all areas are connected using the key nodes in the subgraphs to build a global graph of the whole environment. Finally, the robot can reactively plan a path based on the current environmental situation and predicted human activities. This method enables robots to navigate robustly in a large-scale 3D environment with regular human activities, and it significantly reduces computing workload with proposed RKCM

The impact of interventions on appointment and clinical outcomes for individuals with diabetes: a systematic review

BACKGROUND: Successful diabetes disease management involves routine medical care with individualized patient goals, self-management education and on-going support to reduce complications. Without interventions that facilitate patient scheduling, improve attendance to provider appointments and provide patient information to provider and care team, preventive services cannot begin. This review examines interventions based upon three focus areas: 1) scheduling the patient with their provider; 2) getting the patient to their appointment, and; 3) having patient information integral to their diabetes care available to the provider. This study identifies interventions that improve appointment management and preparation as well as patient clinical and behavioral outcomes. METHODS: A systematic review of the literature was performed using MEDLINE, CINAHL and the Cochrane library. Only articles in English and peer-reviewed articles were chosen. A total of 77 articles were identified that matched the three focus areas of the literature review: 1) on the schedule, 2) to the visit, and 3) patient information. These focus areas were utilized to analyze the literature to determine intervention trends and identify those with improved diabetes clinical and behavioral outcomes. RESULTS: The articles included in this review were published between 1987 and 2013, with 46 of them published after 2006. Forty-two studies considered only Type 2 diabetes, 4 studies considered only Type 1 diabetes, 15 studies considered both Type 1 and Type 2 diabetes, and 16 studies did not mention the diabetes type. Thirty-five of the 77 studies in the review were randomized controlled studies. Interventions that facilitated scheduling patients involved phone reminders, letter reminders, scheduling when necessary while monitoring patients, and open access scheduling. Interventions used to improve attendance were letter reminders, phone reminders, short message service (SMS) reminders, and financial incentives. Interventions that enabled routine exchange of patient information included web-based programs, phone calls, SMS, mail reminders, decision support systems linked to evidence-based treatment guidelines, registries integrated with electronic medical records, and patient health records. CONCLUSIONS: The literature review showed that simple phone and letter reminders for scheduling or prompting of the date and time of an appointment to more complex web-based multidisciplinary programs with patient self-management can have a positive impact on clinical and behavioral outcomes for diabetes patients. Multifaceted interventions aimed at appointment management and preparation during various phases of the medical outpatient care process improves diabetes disease management