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Continuous monitoring of human’s breathing and heart rates is useful in maintaining better health and early detection of many health issues. Designing a technique that can enable contactless and ubiquitous vital sign monitoring is a challenging research problem. This article presents mmVital, a system that uses 60GHz millimeter wave (mmWave) signals for vital sign monitoring. We show that the mmWave signals can be directed to human’s body and the Received Signal Strength (RSS) of the reflections can be analyzed for accurate estimation of breathing and heart rates. We show how the directional beams of mmWave can be used to monitor multiple humans in an indoor space concurrently. mmVital also provides sleep monitoring with sleeping posture identification and detection of central apnea and hypopnea events. It relies on a novel human finding procedure where a human can be located within a room by reflection loss-based object/human classification. We evaluate mmVital using a 60GHz testbed in home and office environment and show that it provides the mean estimation error of 0.43 breaths per minute (Bpm; breathing rate) and 2.15 beats per minute (bpm; heart rate). Also, it can locate the human subject with 98.4% accuracy within 100ms of dwell time on reflection. We also demonstrate that mmVital is effective in monitoring multiple people in parallel and even behind a wall.

Substantial progress in WiFi-based indoor localization has proven that pervasiveness of WiFi can be exploited beyond its traditional use of internet access to enable a variety of sensing applications. Understanding shopper's behavior through physical analytics can provide crucial insights to the business owner in terms of e↵ectiveness of promotions, arrangement of products and e ciency of services. However, analyzing shopper's behavior and browsing patterns is challenging. Since video surveillance can not used due to high cost and privacy concerns, it is necessary to design novel techniques that can provide accurate and e cient view of shopper's behavior. In this work, we propose WiFi-based sensing of shopper's behavior in a retail store. Specifically, we show that various states of a shopper such as standing near the entrance to view a promotion or walking quickly to proceed towards the intended item can be accurately classified by profiling Channel State Information (CSI) of WiFi. We recognize a few representative states of shopper's behavior at the entrance and inside the store, and show how CSI-based profile can be used to detect that a shopper is in one of the states with very high accuracy (⇡ 90%). We discuss the potential and limitations of CSI-based sensing of shopper's behavior and physical analytics in general.

This paper is first of its kind in presenting a detailed characterization of IEEE 802.11ac using real experiments. 802.11ac is the latest WLAN standard that is rapidly being adapted due to its potential to deliver very high throughput. The throughput increase in 802.11ac can be attributed to three factors -larger channel width (80/160 MHz), support for denser modulation (256 QAM) and increased number of spatial streams for MIMO. We provide an experiment evaluation of these factors and their impact using a 18-nodes 802.11ac testbed. Our findings provide numerous insights on benefits and challenges associated with using 802.11ac in practice.Since utilization of larger channel width is one of the most significant changes in 802.11ac, we focus our study on understanding its impact on energy efficiency and interference. Using experiments, we show that utilizing larger channel width is in general less energy efficient due to its higher power consumption in idle listening mode. Increasing the number of MIMO spatial streams is comparatively more energy efficient for achieving the same percentage increase in throughput. We also show that 802.11ac link witnesses severe unfairness issues when it coexists with legacy 802.11. We provide a detailed analysis to show how medium access in heterogeneous channel width environment leads to the unfairness issues. We believe that these and many other findings presented in this work will help in understanding and resolving various performance issues of next generation WLANs.

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Energy Expenditure Estimation (EEE) is an important step in tracking personal activity and preventing chronic diseases such as obesity, diabetes and cardiovascular diseases. Accurate and online EEE utilizing small wearable sensors is a difficult task, primarily because most existing schemes work offline or using heuristics. In this work, we focus on accurate EEE for tracking ambulatory activities (walking, standing, climbing upstairs or downstairs) of a common smartphone user. We used existing smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately detect EEE. Using Artificial Neural Networks, a machine learning technique, we build a generic regression model for EEE that yields upto 89% correlation with actual Energy Expenditure (EE). Using barometer data, in addition to accelerometry is found to significantly improve EEE performance (upto 15%). We compare our results against stateof-the-art Calorimetry Equations (CE) and consumer electronics devices (Fitbit and Nike+ Fuel Band). We were able to demonstrate the superior accuracy achieved by our algorithm. The results were calibrated against COSMED K4b2 calorimeter readings. Categories and Subject Descriptors J.3 [Computer Applications]: Life & medical sciences-Health General Terms Experimentation, Algorithms.