Activity of daily living (ADL) is a significant predictor of the independence and functional capabilities of an individual. Measurements of ADLs help to indicate one’s health status and capabilities of quality living. Recently, the most common ways to capture ADL data are far from automation, including a costly 24/7 observation by a designated caregiver, self-reporting by the user laboriously, or filling out a written ADL survey. Fortunately, ubiquitous sensors exist in our surroundings and on electronic devices in the Internet of Things (IoT) era. We proposed the ADL Recognition System that utilizes the sensor data from a single point of contact, such as smartphones, and conducts time-series sensor fusion processing. Raw data is collected from the ADL Recorder App constantly running on a user’s smartphone with multiple embedded sensors, including the microphone, Wi-Fi scan module, heading orientation of the device, light proximity, step detector, accelerometer, gyroscope, magnetometer, etc. Key technologies in this research cover audio processing, Wi-Fi indoor positioning, proximity sensing localization, and time-series sensor data fusion. By merging the information of multiple sensors, with a time-series error correction technique, the ADL Recognition System is able to accurately profile a person’s ADLs and discover his life patterns. This paper is particularly concerned with the care for the older adults who live independently.

In recent years, when solving MOPs, especially discrete path optimization problems, MOACOs concerning other meta-heuristic algorithms have been used and improved often, and they have become a hot research topic. This article will start from the basic process of ant colony algorithms for solving MOPs to illustrate the differences between each step. Secondly, we provide a relatively complete classification of algorithms from different aspects, in order to more clearly reflect the characteristics of different algorithms. After that, considering the classification result, we have carried out a comparison of some typical algorithms which are from different categories on different sizes TSP (traveling salesman problem) instances and analyzed the results from the perspective of solution quality and convergence rate. Finally, we give some guidance about the selection of these MOACOs to solve problem and some research works for the future.

Crowdsourcing can provide a platform for evaluating software engineering research. In this paper, we aim to explore characteristics of the worker population on Amazon's Mechanical Turk, a popular microtask crowdsourcing environment, and measure the percentage of workers who are potentially qualified to perform software- or computer science-related tasks. Through a baseline survey and two replications, we measure workers' answer consistency as well as the consistency of sample characteristics. In the end, we deployed 1,200 total surveys that were completed by 1,064 unique workers. Our results show that 24% of the study participants have a computer science or IT background and most people are payment driven when choosing tasks. The sample characteristics can vary significantly, even on large samples with 300 participants. Additionally, we often observed inconsistency in workers' answers for those who completed two surveys; approximately 30% answered at least one question inconsistently between the two survey submissions. This implies a need for replication and quality controls in crowdsourced experiments.