Oura is an award-winning and fast-growing startup that helps people track all stages of sleep and activity using the Oura Ring and connected app. By providing daily feedback and practical steps to inspire healthy lifestyles, we've helped hundreds of thousands of people improve their sleep, understand their bodies, and transform their health. We're on a mission to empower every person to own their inner potential, and we're seeking candidates who want to make an impact on our journey. We are looking for a Machine Learning Data Scientist to work in our Research Algorithm Platform squad in North America. Working closely with health and physiology scientists, you will develop machine learned models to capture indicators of health and wellness.
If you list the biggest and fastest-growing technologies over the past decade, artificial intelligence (AI) will inarguably top the charts. The global AI market was valued at 39.9 billion in 2019 and is projected to grow at a CAGR of 42.2% during 2020-2027, according to Grand View Research. AI has found applications in every industry, and the fitness sector is no different. Smart fitness wearables, AI-powered fitness apps, and AI and Machine Learning (ML) for gym management are common use cases of AI in fitness. But not many people thought that there would come a time when AI will be on the verge of replacing personal trainers and fitness coaches.
It can be difficult to understand exactly what's going on inside of a deep learning model, which is a real problem for companies concerned about bias, ethics, and explainability. Now IBM is developing something called AI FactSheets, which it describes as a nutrition label for deep learning that explains how models work and that can also detect bias. AI FactSheets is a new addition to Watson Open Scale that will provide a plain-language description of what's going on inside deep learning models. The software, which is expected to be generally available soon, can work with AI models developed by Watson Studio, or any other AI model accessible from a REST API. After being exposed to the model, AI FactSheets generates a PDF with information about bias, trust, and transparency aspects of a given deep learning model.
Availability of powerful computation and communication technology as well as advances in artificial intelligence enable a new generation of complex, AI-intense systems and applications. Such systems and applications promise exciting improvements on a societal level, yet they also bring with them new challenges for their development. In this paper we argue that significant challenges relate to defining and ensuring behaviour and quality attributes of such systems and applications. We specifically derive four challenge areas from relevant use cases of complex, AI-intense systems and applications related to industry, transportation, and home automation: understanding, determining, and specifying (i) contextual definitions and requirements, (ii) data attributes and requirements, (iii) performance definition and monitoring, and (iv) the impact of human factors on system acceptance and success. Solving these challenges will imply process support that integrates new requirements engineering methods into development approaches for complex, AI-intense systems and applications. We present these challenges in detail and propose a research roadmap.
Mapping of spatial hotspots, i.e., regions with significantly higher rates or probability density of generating certain events (e.g., disease or crime cases), is a important task in diverse societal domains, including public health, public safety, transportation, agriculture, environmental science, etc. Clustering techniques required by these domains differ from traditional clustering methods due to the high economic and social costs of spurious results (e.g., false alarms of crime clusters). As a result, statistical rigor is needed explicitly to control the rate of spurious detections. To address this challenge, techniques for statistically-robust clustering have been extensively studied by the data mining and statistics communities. In this survey we present an up-to-date and detailed review of the models and algorithms developed by this field. We first present a general taxonomy of the clustering process with statistical rigor, covering key steps of data and statistical modeling, region enumeration and maximization, significance testing, and data update. We further discuss different paradigms and methods within each of key steps. Finally, we highlight research gaps and potential future directions, which may serve as a stepping stone in generating new ideas and thoughts in this growing field and beyond.
Remaining useful life (RUL) estimation is a crucial component in the implementation of intelligent predictive maintenance and health management. Deep neural network (DNN) approaches have been proven effective in RUL estimation due to their capacity in handling high-dimensional non-linear degradation features. However, the applications of DNN in practice face two challenges: (a) online update of lifetime information is often unavailable, and (b) uncertainties in predicted values may not be analytically quantified. This paper addresses these issues by developing a hybrid DNN-based prognostic approach, where a Wiener-based-degradation model is enhanced with adaptive drift to characterize the system degradation. An LSTM-CNN encoder-decoder is developed to predict future degradation trajectories by jointly learning noise coefficients as well as drift coefficients, and adaptive drift is updated via Bayesian inference. A computationally efficient algorithm is proposed for the calculation of RUL distributions. Numerical experiments are presented using turbofan engines degradation data to demonstrate the superior accuracy of RUL prediction of our proposed approach.
Inertial Measurement Unit (IMU) sensors are becoming increasingly ubiquitous in everyday devices such as smartphones, fitness watches, etc. As a result, the array of health-related applications that tap onto this data has been growing, as well as the importance of designing accurate prediction models for tasks such as human activity recognition (HAR). However, one important task that has received little attention is the prediction of an individual's heart rate when undergoing a physical activity using IMU data. This could be used, for example, to determine which activities are safe for a person without having him/her actually perform them. We propose a neural architecture for this task composed of convolutional and LSTM layers, similarly to the state-of-the-art techniques for the closely related task of HAR. However, our model includes a convolutional network that extracts, based on sensor data from a previously executed activity, a physical conditioning embedding (PCE) of the individual to be used as the LSTM's initial hidden state. We evaluate the proposed model, dubbed PCE-LSTM, when predicting the heart rate of 23 subjects performing a variety of physical activities from IMU-sensor data available in public datasets (PAMAP2, PPG-DaLiA). For comparison, we use as baselines the only model specifically proposed for this task, and an adapted state-of-the-art model for HAR. PCE-LSTM yields over 10% lower mean absolute error. We demonstrate empirically that this error reduction is in part due to the use of the PCE. Last, we use the two datasets (PPG-DaLiA, WESAD) to show that PCE-LSTM can also be successfully applied when photoplethysmography (PPG) sensors are available to rectify heart rate measurement errors caused by movement, outperforming the state-of-the-art deep learning baselines by more than 30%.
The demand for real-time, affordable, and efficient smart healthcare services is increasing exponentially due to the technological revolution and burst of population. To meet the increasing demands on this critical infrastructure, there is a need for intelligent methods to cope with the existing obstacles in this area. In this regard, edge computing technology can reduce latency and energy consumption by moving processes closer to the data sources in comparison to the traditional centralized cloud and IoT-based healthcare systems. In addition, by bringing automated insights into the smart healthcare systems, artificial intelligence (AI) provides the possibility of detecting and predicting high-risk diseases in advance, decreasing medical costs for patients, and offering efficient treatments. The objective of this article is to highlight the benefits of the adoption of edge intelligent technology, along with AI in smart healthcare systems. Moreover, a novel smart healthcare model is proposed to boost the utilization of AI and edge technology in smart healthcare systems. Additionally, the paper discusses issues and research directions arising when integrating these different technologies together.
Sensors and algorithms are two fundamental elements to construct intelligent systems. The recent progress in machine learning (ML) has produced great advancements in intelligent systems, owing to the powerful data analysis capability of ML algorithms. However, the performance of most systems is still hindered by sensing techniques that typically rely on rigid and bulky sensor devices, which cannot conform to irregularly curved and dynamic surfaces for high‐quality data acquisition. Skin‐like stretchable sensing technology with unique characteristics, such as high conformability, low modulus, and light weight, has been recently developed to solve this issue. Here, the recent progress in the fusion of emerging stretchable electronics and ML technology, for bioelectrical signal recognition, tactile perception, and multimodal integration is summarized, and the challenges and future developments are further discussed.
Researchers at Rensselaer Polytechnic Institute and IBM Research in New York have recently created pFoodReQ, a system that can recommend recipes tailored around the preferences and dietary needs of individual users. This system is outlined in a paper pre-published on arXiv and set to be presented at the 14th International Conference on Web Search and Data Mining (WSDM) in March. "Our work focuses on personalized food recommendation," Mohammed J. Zaki, one of the researchers who developed the system, told TechXplore. "In particular, given a user query in natural language, we want to retrieve the top matches in a recipe dataset." The short-term goal of the study carried out by Zaki and his colleagues was to help people find healthy recipes that satisfy both their dietary needs and inclinations.