A Review of Cognitive Assistants for Healthcare

ACM Computing Surveys · 2021 · 67 citations

• Computer Science
• Artificial Intelligence
• Computer Science

Healthcare cognitive assistants (HCAs) are intelligent systems or agents that interact with users in a context-aware and adaptive manner to improve their health outcomes by augmenting their cognitive abilities or complementing a cognitive impairment. They assist a wide variety of users ranging from patients to their healthcare providers (e.g., general practitioner, specialist, surgeon) in several situations (e.g., remote patient monitoring, emergency response, robotic surgery). While HCAs are critical to ensure personalized, scalable, and efficient healthcare, there exists a knowledge gap in finding the emerging trends, key challenges, design guidelines, and state-of-the-art technologies suitable for developing HCAs. This survey aims to bridge this gap for researchers from multiple domains, including but not limited to cyber-physical systems, artificial intelligence, human-computer interaction, robotics, and smart health. It provides a comprehensive definition of HCAs and outlines a novel, practical categorization of existing HCAs according to their target user role and the underlying application goals. This survey summarizes and assorts existing HCAs based on their characteristic features (i.e., interactive, context-aware, and adaptive) and enabling technological aspects (i.e., sensing, actuation, control, and computation). Finally, it identifies critical research questions and design recommendations to accelerate the development of the next generation of cognitive assistants for healthcare.

A Novel Spatial–Temporal Specification-Based Monitoring System for Smart Cities

IEEE Internet of Things Journal · 2021 · 36 citations

• Computer Science
• Computer Science
• Real-time computing

With the development of the Internet of Things, millions of sensors are being deployed in cities to collect real-time data. This leads to a need for checking city states against city requirements at runtime. In this article, we develop a novel spatial-temporal specification-based monitoring system for smart cities. We first describe a study of over 1000 smart city requirements, some of which cannot be specified using the existing logic, such as the signal temporal logic (STL) and its variants. To tackle this limitation, we develop spatial aggregation STL (SaSTL)-a novel spatial aggregation STL-for the efficient runtime monitoring of safety and performance requirements in smart cities. We develop two new logical operators in SaSTL to augment STL for expressing spatial aggregation and spatial counting characteristics that are commonly found in real city requirements. We define the Boolean and quantitative semantics for SaSTL in support of the analysis of city performance across different periods and locations. We also develop efficient monitoring algorithms that can check the SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed spatially in a city). Additionally, we build an SaSTL-based monitoring tool to support decision making of different stakeholders to specify and runtime monitor their requirements in smart cities. We evaluate our SaSTL monitor by applying it to three case studies with large-scale real city sensing data (e.g., up to 10 000 sensors in one study). The results show that SaSTL has a much higher coverage expressiveness than other spatial-temporal logics, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor improves the safety and performance of smart cities via simulated experiments.

Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review

npj Digital Medicine · 2020 · 109 citations

• Computer Science
• Computer Science
• Applied psychology

= 10). This scoping review highlights the current state of wearable sensors' ability to improve upon traditional eating assessment methods by passively detecting eating activity in naturalistic settings, over long periods of time, and with minimal user interaction. A key challenge in this field, wide variation in eating outcome measures and evaluation metrics, demonstrates the need for the development of a standardized form of comparability among sensors/multi-sensor systems and multidisciplinary collaboration.

SaSTL: Spatial Aggregation Signal Temporal Logic for Runtime Monitoring in Smart Cities

2020 · 40 citations

• Computer Science
• Computer Science
• Theoretical computer science

We present SaSTL-a novel Spatial Aggregation Signal Temporal Logic-for the efficient runtime monitoring of safety and performance requirements in smart cities. We first describe a study of over 1,000 smart city requirements, some of which can not be specified using existing logic such as Signal Temporal Logic (STL) and its variants. To tackle this limitation, we develop two new logical operators in SaSTL to augment STL for expressing spatial aggregation and spatial counting characteristics that are commonly found in real city requirements. We also develop efficient monitoring algorithms that can check a SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed spatially in a city). We evaluate our SaSTL monitor by applying to two case studies with large-scale real city sensing data (e.g., up to 10,000 sensors in one requirement). The results show that SaSTL has a much higher coverage expressiveness than other spatial-temporal logics, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor can help improve the safety and performance of smart cities via simulated experiments.