Wearable Activity Recognition using Invariant Signal Segments

Wearable activity recognition aims to identify and assess human activities with the helpof computer systems by evaluating signals of sensors which can be attached to the humanbody. This provides us with valuable information in several areas: in health care, e.g. fluidand food intake monitoring; in sports, e.g. training support and monitoring; in entertainment,e.g. human-computer interface using body movements; in industrial scenarios, e.g.computer support for detected work tasks. Several challenges exist for wearable activityrecognition: a large number of nonrelevant activities (null class), the evaluation of largenumbers of sensor signals (curse of dimensionality), ambiguity of sensor signals comparedto the activities and finally the high variability of human activity in general.This thesis develops a new activity recognition strategy, called invariants classification,which addresses these challenges, especially the variability in human activities. Thecore idea is that often even highly variable actions include short, more or less invariantsub-actions which are due to hard physical constraints. If someone opens a door, themovement of the hand to the door handle is not fixed. However the door handle has tobe pushed to open the door. The invariants classification algorithm is structured in fourphases: segmentation, invariant identification, classification, and spotting. The segmentationdivides the continuous sensor data stream into meaningful parts, which are relatedto sub-activities. Our segmentation strategy uses the zero crossings of the central differencequotient of the sensor signals, as segment borders. The invariant identification findsthe invariant sub-activities by means of clustering and a selection strategy dependent oncertain features. The classification identifies the segments of a specific activity class, usingmodels generated from the invariant sub-activities. The models include the invariantsub-activity signal and features calculated on sensor signals related to the sub-activity. Inthe spotting, the classified segments are used to find the entire activity class instances inthe continuous sensor data stream. For this purpose, we use the position of the invariantsub-activity in the related activity class instance for the estimation of the borders of theactivity instances.In this thesis, we show that our new activity recognition strategy, built on invariantsub-activities, is beneficial. We tested it on three human activity datasets with wearableinertial measurement units (IMU). Compared to previous publications on the samedatasets we got improvement in the activity recognition in several classes, some with alarge margin. Our segmentation achieves a sensible method to separate the sensor data inrelation to the underlying activities. Relying on sub-activities makes us independent fromimprecise labels on the training data. After the identification of invariant sub-activities,we calculate a value called cluster precision for each sensor signal and each class activity.This tells us which classes can be easily classified and which sensor channels supportthe classification best. Finally, in the training for each activity class, our algorithm selectssuitable signal channels with invariant sub-activities on different points in time andwith different length. This makes our strategy a multi-dimensional asynchronous motifdetection with variable motif length.