Detecting Irregular Patterns in IoT Streaming Data for Fall Detection

Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to enable predictive analytics for automated notification and decision support. In this work, we address the problem of learning an irregular human activity pattern, fall, from streaming IoT data from wearable sensors. We present a deep neural network model for detecting fall based on accelerometer data giving 98.75 percent accuracy using an online physical activity monitoring dataset called "MobiAct", which was published by Vavoulas et al. The initial model was developed using IBM Watson studio and then later transferred and deployed on IBM Cloud with the streaming analytics service supported by IBM Streams for monitoring real-time IoT data. We also present the systems architecture of the real-time fall detection framework that we intend to use with mbientlabs wearable health monitoring sensors for real time patient monitoring at retirement homes or rehabilitation clinics.Comment: 7 page

Machine Learning DDoS Detection for Consumer Internet of Things Devices

An increasing number of Internet of Things (IoT) devices are connecting to the Internet, yet many of these devices are fundamentally insecure, exposing the Internet to a variety of attacks. Botnets such as Mirai have used insecure consumer IoT devices to conduct distributed denial of service (DDoS) attacks on critical Internet infrastructure. This motivates the development of new techniques to automatically detect consumer IoT attack traffic. In this paper, we demonstrate that using IoT-specific network behaviors (e.g. limited number of endpoints and regular time intervals between packets) to inform feature selection can result in high accuracy DDoS detection in IoT network traffic with a variety of machine learning algorithms, including neural networks. These results indicate that home gateway routers or other network middleboxes could automatically detect local IoT device sources of DDoS attacks using low-cost machine learning algorithms and traffic data that is flow-based and protocol-agnostic.Comment: 7 pages, 3 figures, 3 tables, appears in the 2018 Workshop on Deep Learning and Security (DLS '18

Neuropsychiatric correlates of power state in smartphone use

Schizophrenia is a complex and devastating illness with heterogeneous symptoms, late diagnosis, and excess early mortality. It is also associated with comorbidities including substance abuse, depression, anxiety, and sleep problems, that have adverse effects on the individual over the entire course of the disease. While effects of these comorbidities have been identified in the literature, few studies have involved longitudinal assessments and reproducible data collection in large populations. Recent smartphone research tools have been developed that provide better access to patients and can enable a real-world snapshot of a person’s mental state. In addition, these tools use the phone’s sensors to construct a digital phenotype of an individual, with the potential to detect changes in symptoms and cognition on a moment-by-moment basis. Previous studies report associations between anxiety and smartphone use, but most involve cross-sectional data and cohorts of healthy controls completing paper and pencil scales. A recent smartphone study collected day-to-day symptomatology, cognition, and phone usage data and discovered that the association between anxiety and smartphone use is more complex than originally thought