A Natural Language Query Interface for Searching Personal Information on Smartwatches

Currently, personal assistant systems, run on smartphones and use natural language interfaces. However, these systems rely mostly on the web for finding information. Mobile and wearable devices can collect an enormous amount of contextual personal data such as sleep and physical activities. These information objects and their applications are known as quantified-self, mobile health or personal informatics, and they can be used to provide a deeper insight into our behavior. To our knowledge, existing personal assistant systems do not support all types of quantified-self queries. In response to this, we have undertaken a user study to analyze a set of “textual questions/queries” that users have used to search their quantified-self or mobile health data. Through analyzing these questions, we have constructed a light-weight natural language based query interface - including a text parser algorithm and a user interface - to process the users’ queries that have been used for searching quantified-self information. This query interface has been designed to operate on small devices, i.e. smartwatches, as well as augmenting the personal assistant systems by allowing them to process end users’ natural language queries about their quantified-self data

Nocloud: Experimenting with Network Disconnection by Design

Application developers often advocate uploading data to the cloud for analysis or storage, primarily due to concerns about the limited computational capability of ubiquitous devices. Today, however, many such devices can still effectively operate and execute complex algorithms without reliance on the cloud. The authors recommend prioritizing on-device analysis over uploading the data to another host, and if on-device analysis is not possible, favoring local network services over a cloud service

VieLens,: an interactive search engine for LSC2019

With the appearance of many wearable devices like smartwatches, recording glasses (such as Google glass), smart phones, digital personal profiles have become more readily available nowadays. However, searching and navigating these multi-source, multi-modal, and often unstructured data to extract useful information is still a relatively challenging task. Therefore, the LSC2019 competition has been organized so that researchers can demonstrate novel search engines, as well as exchange ideas and collaborate on these types of problems. We present in this paper our approach for supporting interactive searches of lifelog data by employing a new retrieval system called VieLens, which is an interactive retrieval system enhanced by natural language processing techniques to extend and improve search results mainly in the context of a user’s activities in their daily life

Toward Accurate and Efficient Feature Selection for Speaker Recognition on Wearables

Due to the user-interface limitations of wearable devices, voice-based interfaces are becoming more common; speaker recognition may then address the authentication requirements of wearable applications. Wearable devices have small form factor, limited energy budget and limited computational capacity. In this paper, we examine the challenge of computing speaker recognition on small wearable platforms, and specifically, reducing resource use (energy use, response time) by trimming the input through careful feature selections. For our experiments, we analyze four different feature-selection algorithms and three different feature sets for speaker identification and speaker verification. Our results show that Principal Component Analysis (PCA) with frequency-domain features had the highest accuracy, Pearson Correlation (PC) with time-domain features had the lowest energy use, and recursive feature elimination (RFE) with frequency-domain features had the least latency. Our results can guide developers to choose feature sets and configurations for speaker-authentication algorithms on wearable platforms

Using Lucene for Developing a Question-Answering Agent in Portuguese

Given the limitations of available platforms for creating conversational agents, and that a question-answering agent suffices in many scenarios, we take advantage of the Information Retrieval library Lucene for developing such an agent for Portuguese. The solution described answers natural language questions based on an indexed list of FAQs. Its adaptation to different domains is a matter of changing the underlying list. Different configurations of this solution, mostly on the language analysis level, resulted in different search strategies, which were tested for answering questions about the economic activity in Portugal. In addition to comparing the different search strategies, we concluded that, towards better answers, it is fruitful to combine the results of different strategies with a voting method

From Palm to Arm

The number of people diagnosed with diabetes is increasing at an alarming rate. However, strong evidence shows that health information technology has improved medical outcomes, especially within the field of diabetes research. This thesis investigates how to motivate people with diabetes to perform self-management activities with the help of a smartwatch application. The work is grounded in a literature review, discovering how people manage diabetes with smartwatches today and the lack of existing motivational features on existing solutions. As a result, a system design of a smartwatch application is presented, including a graphical user interface (UI). The system aims to manage and monitor the essential diabetes metrics: nutrition, blood glucose, and physical activity while generating motivation through goal setting. In addition, the presented system is oriented on a standalone architecture, removing the need to pair a smartphone to the smartwatch and introducing novel features for smartwatch diabetes management. Finally, a proof of concept is implemented using Android studio to solidify the systems requirements. Furthermore, a descriptive analysis of a survey presents that among people with diabetes, simplicity is the most crucial factor in smartwatch applications. Based on this, the presented UI is evaluated according to the simplicity of other systems and the impact the motivational features have on the system’s complexity. Then, the potential of a standalone architecture for diabetes management is discussed. Finally, it is concluded that goal-setting features should be more widely used among smartwatch applications due to their low impact on the application. The future work of the thesis is to test the system on people with diabetes. Both to evaluate the system useability scale and observe the impact goal-setting has on performing diabetes self-management. Furthermore, in this thesis, it is assumed that there is a communication channel between diabetes devices and the smartwatch. This must be further investigated with the next generation of diabetes devices

Development and comparison of customized voice-assistant systems for independent living older adults

Voice-controlled in-home personal assistants have great potential to assist older adults. This thesis explores the aspects of human-computer interface design, specifically a voice assistant, to help older adults manage their personal health, especially in the case of chronic health conditions. In our previous work, we have built a web interface for caregivers to monitor older adults' health changes based on in-home sensor data from motion sensors, bed sensors, and depth sensors. Data collected from these sensors are stored in servers and processed using several algorithms to obtain health and activity parameters including gait, fall risk, detect fall, motion patterns, sleep, heart rate, and respiration rate, as well as to generate health alerts. The sensor system with automated health alerts and care coordination has been shown to help seniors maintain better functionality. In our current research project, we focus on developing a consumer interface for older adults and their designated trusted others that can provide health information on-demand, based on spoken queries. The health information is presented as both audio and visual displays and uses graphical visualizations and linguistic summaries of the sensor data trends and changes. The goal is to present data in a form that is simple to understand. To accomplish our objective of creating an easy-to-use-and-understand health data interface for older adults, we explore voice-controlled, in-home personal assistants as a solution. Two voice assistant platforms with displays were selected for implementation and testing, namely, the Amazon Echo Show and the Lenovo Smart Display with Google Assistant.by Shradha ShaliniIncludes bibliographical reference