Wearable sensor badge and sensor jacket for context awareness

Abstract

The addition of sensors to wearable computers allows them to adapt their functions to more suit the activities and situation of their wearers. Here a wearable sensor badge is described constructed from (hard) electronic components, which can sense perambulatory activities for context-awareness. A wearable sensor jacket is described that uses advanced knitting techniques to form (soft) fabric stretch sensors positioned to measure upper limb and body movement. Worn on-the-hip, or worn as clothing, these unobtrusive sensors supply abstract information about your current activity to your other wearable computers.

Preprocessing techniques for context recognition from accelerometer data

“…Several researchers have used the mean to either directly or indirectly identify user posture (sitting, standing or lying) [11,19,22,23] and also to discriminate the type of activity as either dynamic or static [60]. Others have used the mean as input to classifiers like Neural Networks [51,59], Naive Bayes [27], Kohonen Self-Organizing Maps [29], Decision Trees [5], and even Fuzzy Inference [20].…”

“…The range (the difference between maximum and minimum sample values) was used in [11] together with other indicators to discriminate between walking and running. Application of the maximum and minimum values in accelerometer-based systems was explored to detect steps with the Twiddler keyboard [4], to detect gestures as mnemonical body shortcuts [13], and in activity recognition as an input to a Neural Network classifier [59].…”

“…[11]) to distinguish walking from running. Zero-crossings rate is the rate of zero-crossings along a signal and is commonly applied to audio signals to identify the surrounding environment [10] or the type of sound such as music, speech, and noise [49].…”

Sensor Fusion for Semantic Place Labeling

“…Other studies on semantic place labeling so far [Reddy et al, 2010, Consolvo et al, 2008, Arase et al, 2010, Bouten et al, 1997, Perrin et al, 2000, Junker et al, 2004, Preece et al, 2009, Berchtold et al, 2010, Ravi et al, 2005, Bao and Intille, 2004, Chang et al, 2007, Farringdon et al, 1999, Kern et al, 2003, Mantyjarvi et al, 2001, Stikic et al, 2008, Zinnen et al, 2009, Lester et al, 2005, Siewiorek et al, 2003 are mostly based on unlabeled data or on a small number of sensor and state data. The field of physical activity recognition based on accelerometer sensor data is well researched [Consolvo et al, 2008, Arase et al, 2010, Berchtold et al, 2010, Bao and Intille, 2004, Farringdon et al, 1999, Kern et al, 2003].…”

“…The field of physical activity recognition based on accelerometer sensor data is well researched [Consolvo et al, 2008, Arase et al, 2010, Berchtold et al, 2010, Bao and Intille, 2004, Farringdon et al, 1999, Kern et al, 2003]. Accuracies of physical activity recognition could be achieved up to 90% [Reddy et al, 2010,Preece et al, 2009,Ravi et al, 2005, Bao and Intille, 2004, Chang et al, 2007, Mantyjarvi et al, 2001], but the current average smartphone has more sensors built in than only a accelorometer.…”