Proceedings ArticleDOI
SoundSense: scalable sound sensing for people-centric applications on mobile phones
Hong Lu,Wei Pan,Nicholas D. Lane,Tanzeem Choudhury,Andrew T. Campbell +4 more
- pp 165-178
TLDR
This paper proposes SoundSense, a scalable framework for modeling sound events on mobile phones that represents the first general purpose sound sensing system specifically designed to work on resource limited phones and demonstrates that SoundSense is capable of recognizing meaningful sound events that occur in users' everyday lives.Abstract:
Top end mobile phones include a number of specialized (e.g., accelerometer, compass, GPS) and general purpose sensors (e.g., microphone, camera) that enable new people-centric sensing applications. Perhaps the most ubiquitous and unexploited sensor on mobile phones is the microphone - a powerful sensor that is capable of making sophisticated inferences about human activity, location, and social events from sound. In this paper, we exploit this untapped sensor not in the context of human communications but as an enabler of new sensing applications. We propose SoundSense, a scalable framework for modeling sound events on mobile phones. SoundSense is implemented on the Apple iPhone and represents the first general purpose sound sensing system specifically designed to work on resource limited phones. The architecture and algorithms are designed for scalability and Soundsense uses a combination of supervised and unsupervised learning techniques to classify both general sound types (e.g., music, voice) and discover novel sound events specific to individual users. The system runs solely on the mobile phone with no back-end interactions. Through implementation and evaluation of two proof of concept people-centric sensing applications, we demostrate that SoundSense is capable of recognizing meaningful sound events that occur in users' everyday lives.read more
Citations
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Proceedings ArticleDOI
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices
TL;DR: Crowd-ML as mentioned in this paper is a privacy-preserving machine learning framework for a crowd of smart devices, which can solve a wide range of learning problems for crowd sensing data with differential privacy guarantees.
Proceedings ArticleDOI
mConverse: inferring conversation episodes from respiratory measurements collected in the field
TL;DR: In this article, the authors presented mConverse, a mobile-based system to infer conversation episodes from respiration measurements collected in the field from an unobtrusively wearable respiratory inductive plethysmograph (RIP) band worn around the user's chest.
Proceedings ArticleDOI
Inferring human mobility patterns from taxicab location traces
TL;DR: It is shown that while past approaches are effective in detecting hotspots using location traces, they are largely ineffective in identifying trips (pairs of pickup and dropoff points) and proposed the use of a graph theory concept - stretch factor in a novel manner to identify trip(s) made by a taxicab.
Journal ArticleDOI
Recognition and Repetition Counting for ComplexPhysical Exercises with Deep Learning.
TL;DR: This paper presents an end-to-end deep learning approach, able to provide probability distributions over activities from raw sensor data, and applies it to 10 complex full-body exercises typical in CrossFit, achieving classification accuracy of 99.96%.
Proceedings ArticleDOI
ContextSense: unobtrusive discovery of incremental social context using dynamic bluetooth data
Zhenyu Chen,Yiqiang Chen,Lisha Hu,Shuangquan Wang,Xinlong Jiang,Xiaojuan Ma,Nicholas D. Lane,Andrew T. Campbell +7 more
TL;DR: Experimental results show that ContextSense can automatically cope with "incremental social context" classes that appear unpredictably in the real-world, and an ELM-based learning method for continuous and unobtrusive discovery of new social contexts incrementally from dynamic bluetooth data.
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