The Silent Language

Some patients, especially patients with chronic diseases such as heart disease, require continuous monitoring of their condition. Wearable devices for patient monitoring were introduced many years ago – for instance, wearable ambulatory ECG (electrocardiogram) recorders commonly known as Holter monitors (Figure 1) are used for monitoring cardiac patients. However, these monitors are quite bulky and can only record the signal for a limited time. Patients are often asked to wear a Holter monitor for a few days and then return to the clinic for diagnosis. This often overlooks transient but life-threatening events. In addition, we don’t know under what condition the signals are acquired, and this often leads to false alarms. For example, a sudden rise in heart rate may be caused by emotion, such as watching a horror movie, or by exercise, rather than by a heart condition. Body sensing To address these issues, the concept of Body Sensor Networks (BSN) was first proposed in 2002 by Prof. Guang-Zhong Yang from Imperial College London. The aim of the BSN is to provide a truly personalised monitoring platform that is pervasive, intelligent, and Figure 1 A patient wearing a Holter monitor

Body sensor networks

https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Wozniak%20-%20Multiple%20Classifier%20Systems%20-%202014.pdf

A survey of multiple classifier systems as hybrid systems

The evolution of ubiquitous sensing technologies has led to intelligent environments that can monitor and react to our daily activities, such as adapting our heating and cooling systems, responding to our gestures, and monitoring our elderly. In this paper, we ask whether it is possible for smart environments to monitor our vital signs remotely, without instrumenting our bodies. We introduce Vital-Radio, a wireless sensing technology that monitors breathing and heart rate without body contact. Vital-Radio exploits the fact that wireless signals are affected by motion in the environment, including chest movements due to inhaling and exhaling and skin vibrations due to heartbeats. We describe the operation of Vital-Radio and demonstrate through a user study that it can track users' breathing and heart rates with a median accuracy of 99%, even when users are 8~meters away from the device, or in a different room. Furthermore, it can monitor the vital signs of multiple people simultaneously. We envision that Vital-Radio can enable smart homes that monitor people's vital signs without body instrumentation, and actively contribute to their inhabitants' well-being.

/pdf/smart-homes-that-monitor-breathing-and-heart-rate-425fzf6qc1.pdf

Smart Homes that Monitor Breathing and Heart Rate

https://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/1371_2011-pr-galar-ovo-ova.pdf

An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes

The rapid adoption of smartphones along with a growing habit for using these devices as alarm clocks presents an opportunity to use this device as a sleep detector. This adds value to UbiComp and personal informatics in terms of user context and new performance data to collect and visualize, and it benefits healthcare as sleep is correlated with many health issues. To assess this opportunity, we collected one month of phone sensor and sleep diary entries from 27 people who have a variety of sleep contexts. We used this data to construct models that detect sleep and wake states, daily sleep quality, and global sleep quality. Our system classifies sleep state with 93.06% accuracy, daily sleep quality with 83.97% accuracy, and overall sleep quality with 81.48% accuracy. Individual models performed better than generally trained models, where the individual models require 3 days of ground truth data and 3 weeks of ground truth data to perform well on detecting sleep and sleep quality, respectively. Finally, the features of noise and movement were useful to infer sleep quality.

/pdf/toss-n-turn-smartphone-as-sleep-and-sleep-quality-detector-4edwn7oh4o.pdf

Toss 'n' turn: smartphone as sleep and sleep quality detector

http://sclab.yonsei.ac.kr/publications/Papers/IJ/2008_PR_JHHong.pdf

Fingerprint classification using one-vs-all support vector machines dynamically ordered with naïve Bayes classifiers

People engage with many overlapping social networks and enact diverse social roles across different facets of their lives. Unfortunately, many online social networking services reduce most people's contacts to "friend". A richer computational model of relationships would be useful for a number of applications such as managing privacy settings and organizing communications. In this paper, we take a step towards a richer computational model by using call and text message logs from mobile phones to classifying contacts according to life facet (family, work, and social). We extract various features such as communication intensity, regularity, medium, and temporal tendency, and classify the relationships using machine-learning techniques. Our experimental results on 40 users showed that we could classify life facets with up to 90.5% accuracy. The most relevant features include call duration, channel selection, and time of day for the communication.

Mining smartphone data to classify life-facets of social relationships

How effective are call and SMS logs in modeling tie strength? Frequency and duration of communication has long been cited as a major aspect of tie strength. Intuitively, this makes sense: people communicate with those that they feel close to. Highly cited research papers have pushed this idea further, using communication as a direct proxy for tie strength. However, this operationalization has not been validated. Our work evaluates this assumption. We collected call and SMS logs and ground truth relationship data from 36 participants. Consistent with theory, we found that frequent or long-duration communication likely indicates a strong tie. However, the use of call and SMS logs produced many errors in separating strong and weak ties, suggesting this approach is incomplete. Follow-up interviews indicate fundamental challenges for inferring tie strength from communication logs.

http://www.cs.cmu.edu/~jwwiese/publications/tiestrength.pdf

"You Never Call, You Never Write": Call and SMS Logs Do Not Always Indicate Tie Strength

Major Depressive Disorder (MDD) is the most common mental health disorder and remains a leading cause of disability and lost productivity with huge costs for society. MDD has high rates of relapse and recurrence, and it has strong correlations with feelings of low social support and disrupted sleep. However, MDD is also commonly misdiagnosed by primary care providers, which leads to delayed treatment and unnecessary suffering. Changes in technology now make it possible to cheaply and effectively monitor social and sleep behaviors, offering the potential of early detection of the onset of MDD. We report on the design of Big Black Dog, a smartphone-based system for gathering data about social and sleep behaviors. We also report on the results of a pilot evaluation to understand the feasibility of gathering and using data from smartphones for inferring the onset of depression.

/pdf/detection-of-behavior-change-in-people-with-depression-lacbm2y3ul.pdf

Jun-Ki Min

Papers

Toss 'n' turn: smartphone as sleep and sleep quality detector

Fingerprint classification using one-vs-all support vector machines dynamically ordered with naïve Bayes classifiers

Mining smartphone data to classify life-facets of social relationships

"You Never Call, You Never Write": Call and SMS Logs Do Not Always Indicate Tie Strength

Detection of behavior change in people with depression