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66
Pbn: Towards practical activity recognition using smartphone-based body sensor networks
- In Proc. of Sensys ’11
"... Thevastarrayofsmallwirelesssensorsisaboontobody sensornetworkapplications,especiallyinthecontextawareness and activity recognition arena. However, most activity recognition deployments and applications are challenged to provide personal control and practical functionality for everyday use. We argue ..."
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Cited by 30 (4 self)
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Thevastarrayofsmallwirelesssensorsisaboontobody sensornetworkapplications,especiallyinthecontextawareness and activity recognition arena. However, most activity recognition deployments and applications are challenged to provide personal control and practical functionality for everyday use. We argue that activity recognition for mobile devices must meet several goals in order to provide a practical solution: user friendlyhardwareandsoftware,accurate and efficient classification, and reduced reliance on ground truth. To meet these challenges, we present PBN: Practical Body Networking. Through the unification of TinyOS motes and Android smartphones, we combine the sensing power of on-bodywireless sensors with the additionalsensing power, computationalresources, and user-friendlyinterfaceof anAndroidsmartphone. We provideanaccurateand efficientclassificationapproachthroughtheuseofensemble learning. We explore the propertiesof different sensors and sensor data to further improve classification efficiency and reducerelianceon user annotatedgroundtruth. We evaluate our PBN system with multiple subjectsoveratwo week periodanddemonstratethatthesystemiseasytouse,accurate, andappropriateformobiledevices. Categories andSubject Descriptors C.3 [Special-Purpose and Application-Based Sys-
IODetector: A generic service for indoor outdoor detection
- In SenSys’ 12
"... The location and context switching, especially the indoor/outdoor switching, provides essential and primitive information for upper layer mobile applications. In this paper, we present IODetector: a lightweight sensing service which runs on the mobile phone and detects the indoor/outdoor environment ..."
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Cited by 19 (5 self)
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The location and context switching, especially the indoor/outdoor switching, provides essential and primitive information for upper layer mobile applications. In this paper, we present IODetector: a lightweight sensing service which runs on the mobile phone and detects the indoor/outdoor environment in a fast, accurate, and efficient manner. Constrained by the energy budget, IODetector leverages primarily lightweight sensing resources including light sensors, magnetism sensors, celltower signals, etc. For universal applicability, IODetector assumes no prior knowledge (e.g., fingerprints) of the environment and uses only on-board sensors common to mainstream mobile phones. Being a generic and lightweight service component, IODetector greatly benefits many location-based and context-aware applications. We prototype the IODetector on Android mobile phones and evaluate the system comprehensively with data collected from 19 traces which include 84 different places during one month period, employing different phone models. We further perform a case study where we make use of IODetector to instantly infer the GPS availability and localization accuracy in different indoor/outdoor environments.
Accelerometer-based transportation mode detection on smartphones
- In Proceedings of ACM Conference on Embedded Networked Sensor Systems (SenSys
, 2013
"... We present novel accelerometer-based techniques for accu-rate and fine-grained detection of transportation modes on smartphones. The primary contributions of our work are an improved algorithm for estimating the gravity component of accelerometer measurements, a novel set of accelerometer features t ..."
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Cited by 17 (0 self)
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We present novel accelerometer-based techniques for accu-rate and fine-grained detection of transportation modes on smartphones. The primary contributions of our work are an improved algorithm for estimating the gravity component of accelerometer measurements, a novel set of accelerometer features that are able to capture key characteristics of vehic-ular movement patterns, and a hierarchical decomposition of the detection task. We evaluate our approach using over 150 hours of transportation data, which has been collected from 4 different countries and 16 individuals. Results of the evaluation demonstrate that our approach is able to im-prove transportation mode detection by over 20 % compared to current accelerometer-based systems, while at the same time improving generalization and robustness of the detec-tion. The main performance improvements are obtained for motorised transportation modalities, which currently repre-sent the main challenge for smartphone-based transporta-tion mode detection.
Human-centric Sensing
"... The first decade of the century witnessed a proliferation of devices with sensing and communication capabilities in the possession of the average individual. Examples range from camera phones and wireless GPS units to sensor-equipped, networked fitness devices and entertainment platforms (such as Wi ..."
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Cited by 15 (6 self)
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The first decade of the century witnessed a proliferation of devices with sensing and communication capabilities in the possession of the average individual. Examples range from camera phones and wireless GPS units to sensor-equipped, networked fitness devices and entertainment platforms (such as Wii). Social networking platforms emerged, such as Twitter, that allow sharing information in real time. The unprecedented deployment scale of such sensors and connectivity options usher in an era of novel data-driven applications that rely on inputs collected by networks of humans or measured by sensors acting on their behalf. These applications will impact domains as diverse as health, transportation, energy, disaster recovery, intelligence, and warfare. This paper surveys the important opportunities in human-centric sensing, identifies challenges brought about by such opportunities, and describes emerging solutions to these challenges. 1.
ParkSense: A Smartphone Based Sensing System For On-Street Parking
"... Studies of automotive traffic have shown that on average 30 % of traffic in congested urban areas is due to cruising drivers looking for parking. While we have witnessed a push towards sensing technologies to monitor real-time parking availability, instrumenting on-street parking throughout a city i ..."
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Cited by 13 (0 self)
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Studies of automotive traffic have shown that on average 30 % of traffic in congested urban areas is due to cruising drivers looking for parking. While we have witnessed a push towards sensing technologies to monitor real-time parking availability, instrumenting on-street parking throughout a city is a considerable investment. In this paper, we present ParkSense, a smartphone based sensing system that detects if a driver has vacated a parking spot. ParkSense leverages the ubiquitous Wi-Fi beacons in urban areas for sensing unparking events. It utilizes a robust Wi-Fi signature matching approach to detect driver’s return to the parked vehicle. Moreover, it uses a novel approach based on the rate of change of Wi-Fi beacons to sense if the user has started driving. We show that the rate of change of the observed beacons is highly correlated with actual user speed and is a good indicator of whether a user is in a vehi-cle. Through empirical evaluation, we demonstrate that our approach has a significantly smaller energy footprint than traditional location sensors like GPS and Wi-Fi based posi-tioning while still maintaining sufficient accuracy.
SNIP: A Sensor Node-Initiated Probing Mechanism for Opportunistic Data Collection in Sparse Wireless Sensor Networks
- Proc. 1st Int. Workshop on Cyber-Physical Networking Systems, April 10–15
, 2011
"... Abstract-In many potential wireless sensor network applications, the cost of the base station infrastructure can be prohibitive. Instead, we consider the opportunistic use of mobile devices carried by people in daily life to collect sensor data. As the movement of these mobile nodes is by definitio ..."
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Cited by 10 (7 self)
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Abstract-In many potential wireless sensor network applications, the cost of the base station infrastructure can be prohibitive. Instead, we consider the opportunistic use of mobile devices carried by people in daily life to collect sensor data. As the movement of these mobile nodes is by definition uncontrolled, contact probing is a challenging task, particularly for sensor nodes which need to be duty-cycled to achieve long life. We propose a Sensor Node-Initiated Probing mechanism for improving the contact capacity when the duty cycle of a sensor node is fixed. In contrast to existing mobile node-initiated probing mechanisms, in which the mobile node broadcasts a beacon periodically, in SNIP the sensor node broadcasts a beacon each time its radio is turned on according to its duty cycle. We study SNIP through both analysis and network simulation. The evaluation results indicate that SNIP performs much better than mobile-initiated probing. When the fixed duty cycle is lower than 1%, the probed contact capacity can be increased by an order of 2-10; alternatively, SNIP can achieve the same amount of probed contact capacity with much less energy consumption.
The Latency, Accuracy, and Battery (LAB) abstraction: Programmer productivity and energy efficiency for continuous mobile context sensing
- In OOPSLA
, 2013
"... Abstract Emerging mobile applications that sense context are poised to delight and entertain us with timely news and events, health tracking, and social connections. Unfortunately, sensing algorithms quickly drain the phone's battery. Developers can overcome battery drain by carefully optimizi ..."
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Cited by 10 (1 self)
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Abstract Emerging mobile applications that sense context are poised to delight and entertain us with timely news and events, health tracking, and social connections. Unfortunately, sensing algorithms quickly drain the phone's battery. Developers can overcome battery drain by carefully optimizing context sensing but that makes programming with context arduous and ties applications to current sensing hardware. These types of applications embody a twist on the classic tension between programmer productivity and performance due to their combination of requirements. This paper identifies the latency, accuracy, battery (LAB) abstraction to resolve this tension. We implement and evaluate LAB in a system called Senergy. Developers specify their LAB requirements independent of inference algorithms and sensors. Senergy delivers energy efficient context while meeting the requirements and adapts as hardware changes. We demonstrate LAB's expressiveness by using it to implement 22 context sensing algorithms for four types of context (location, driving, walking, and stationary) and six diverse applications. To demonstrate LAB's energy optimizations, we show often an order of magnitude improvements in energy efficiency on applications compared to prior approaches. This relatively simple, priority based API, may serve as a blueprint for future API design in an increasingly complex design space that must tradeoff latency, accuracy, and efficiency to meet application needs and attain portability across evolving, sensor-rich, heterogeneous, and power constrained hardware.
MAQS: a personalized mobile sensing system for indoor air quality monitoring.
- Proc. UbiComp ’11,
, 2011
"... ABSTRACT Most people spend more than 90% of their time indoors; indoor air quality (IAQ) influences human health, safety, productivity, and comfort. This paper describes MAQS, a personalized mobile sensing system for IAQ monitoring. In contrast with existing stationary or outdoor air quality sensin ..."
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Cited by 9 (0 self)
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ABSTRACT Most people spend more than 90% of their time indoors; indoor air quality (IAQ) influences human health, safety, productivity, and comfort. This paper describes MAQS, a personalized mobile sensing system for IAQ monitoring. In contrast with existing stationary or outdoor air quality sensing systems, MAQS users carry portable, indoor location tracking sensors that provide personalized IAQ information. To improve accuracy and energy efficiency, MAQS incorporates three novel techniques: (1) an accurate temporal n-gram augmented Bayesian room localization method that requires few Wi-Fi fingerprints; (2) an air exchange rate based IAQ sensing method, which measures general IAQ using only CO 2 sensors; and (3) a zone-based proximity detection method for collaborative sensing, which saves energy and enables data sharing among users. MAQS has been deployed and evaluated via user study. Detailed evaluation results demonstrate that MAQS supports accurate personalized IAQ monitoring and quantitative analysis with high energy efficiency.
ohmage: An Open Mobile System for Activity and Experience Sampling
"... Advances in technology and infrastructure have positioned mobile phones as a convenient platform for real-time assessment of an individuals health and behavior, while offering unprecedented accessibility and affordability to both the producers and the consumers of the data. In this paper we address ..."
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Cited by 7 (0 self)
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Advances in technology and infrastructure have positioned mobile phones as a convenient platform for real-time assessment of an individuals health and behavior, while offering unprecedented accessibility and affordability to both the producers and the consumers of the data. In this paper we address several of the key challenges that arise in leveraging smartphones for health: designing the complex set of building blocks required for an end-to-end system, motivating participants to sustain engagement in long-lived data collection, and interpreting both the data and the quality of the data collected. We present ohmage, a mobile to web platform that records, analyzes, and visualizes data from both prompted experience samples entered by the user, as well as continuous streams of data passively collected from sensors onboard the mobile device. In order to address the system design and participation motivation challenges, we have incorporated feedback from hundreds of behavioral and technology researchers, focus group participants, and end-users of the system in an iterative design process. ohmage additionally includes rich system and user analytics to instrument the act of participation itself and ultimately to contextualize and better understand the factors affecting the quality of collected data over time. We evaluate the usability and feasibility of
Improving Energy Efficiency of Personal Sensing Applications with Heterogeneous Multi-Processors
"... The availability of multiple sensors on mobile devices offers a significant new capability to enable rich user and context aware applications. Many of these applications run in the background to continuously sense user context. However, running these applications on mobile devices can impose a signi ..."
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Cited by 7 (0 self)
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The availability of multiple sensors on mobile devices offers a significant new capability to enable rich user and context aware applications. Many of these applications run in the background to continuously sense user context. However, running these applications on mobile devices can impose a significant stress on the battery life, and the use of supplementary low-power processors has been proposed on mobile devices for continuous background activities. In this paper, we experimentally and analytically investigate the design considerations that arise in the efficient use of the low power processor and provide a thorough understanding of the problem space. We answer fundamental questions such as which segments of the application are most efficient to be hosted on the low power processor, and how to select an appropriate low power processor. We discuss our measurements, analysis, and results using multiple low power processors and existing phone platforms.
