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1、Gesture Spotting Using Wrist Worn Microphoneand 3-Axis Accelerometer（原文2）Abstract. We perform continuous activity recognition using only two wrist-worn sensors - a 3-axis accelerometer and a microphone. We build on the intuitive notion that two very dierent sensors are unlikely to agree in classicat
2、ion of a false activity. By comparing imperfect, sliding window classications from each of these sensors, we are able discern activities of interest from null or uninteresting activities. Where one sensor alone is unable to perform such partitioning, using comparison we are able to report good overa
3、ll system performance of up to 70% accuracy. In presenting these results, we attempt to give a more-in depth visualization of the errors than can be gathered from confusion matrices alone.1 IntroductionHand actions play a crucial role in most human activities.As a consequences detecting and recognis
4、ing such activities is one of the most important aspects of context recognition. At the same it is one of the most dicult. This is particularly true for continuous recognition where a set of relevant hand motions (gestures) need to be spotted in a data stream. The diculties of such recognition stem
5、from two things.First, dueto a large number of degrees of freedom, hand motions tend to be very diverse. The same activity might be performed in many dierent ways even by a single person. Second, in terms of motion, hands are the most active body parts. We move our hands continuously, mostly in an u
6、nstructured way, even when not doing anything particular with them. In fact in most situations such unstructured motions by far outnumber gestures that are relevant for context recognition. This means that a continuous gesture spotting applications has to deal with an zero class that is dicult to mo
7、del while taking up most of the signal.1.1 Paper ContributionsOur group has invested a considerable amount of work into hand gesture spotting. To date this work has focused on using several sensors distributed over the userfi body to maximise system performance. This included motion sensors (3 axis
8、accelerometer, 3 axis gyroscopes and 3 axis magnetic sensors) on the upper and lower arm 3, microphone/accelerometer combination on the upper and lower arm 5 as well as, more recently, a combination of several motion sensors and ultrasonic location devices.This paper investigates the performance of
9、a gesture spotting system based on a single, wrist mounted device. The idea behind the work is that wrist mounted accessories are broadly accepted and worn by most people on daily basis. In contrast,systems that require the user to put on several sensors at locations such as the upper arm would have
10、 much more problems with user acceptance.The downside of this approach is the reduced amount of information available for the recognition. This for example means that the method of analysing sound intensity dierences between microphones on dierent parts of the bodythat was the corner stone of our pr
11、evious signal partitioning work is not feasible. This problem is compounded by the fact that for the approach to make sense that wrist mounted device can neither contain too manysensors nor can it require computing and/or communication power that would imply large, bulky batteries.The main contribut
12、ion of the paper is to show that, for a certain subset of activities, reasonable gesture spotting results can be achieved with a combination of a microphone and 3 axis accelerometer mounted on the wrist. Our method relies on simple jumping window sound processing algorithms that we have shown 10 to
13、require only minimal computationaland communication performance. For the acceleration we use inference on Hidden Markov Models (HMM), again on jumping windows across the data.To our knowledge this is the rst time that such a simple system and a straight forward jumping window method has been success
14、fully used for hand gesture spotting in continuous data stream with a dominant, unstructured zero class.Previously such setups and algorithms have only been shown to be successfull either for segmented recognition or for scenarios where the zero class was either easy to model or not relevant (e.g. r
15、ecognition of standing, sitting, walking, running 6, 9,12). Where these approaches use acceleration sensors, in the work of ?, ? sound was exploited for performing situation analysis in the wearable computing domain. Also ? used sound information to improve the performance of hearing aids. Complimen
16、tary information from sound and acceleration has been used before to detect defects in material surfaces, e.g. in 13, but no work that the authors are aware uses these for recognition of complex activities.In the paper we summarise the sound and acceleration algorithms and then focus on the performance of dierent fusion methods. It is shown that appropriate fusion is the key to achieving good performance despite simple sensors and algorithms. We verify our
