Rhythm Modelling of Long-Term Activity Data

Long-term monitoring for activity recognition opens up new possibilities for deriving characteristics from the data, such as daily activity rhythms and certain quality measures for the activity performed or for identifying similarities or differences in daily routines. This thesis investigates the detection of activities with wearable sensors and addresses two major challenges in particular: The modelling of a person’s behaviour into rhythmic patterns and the detection of high-level activities, e.g., having lunch or sleeping. To meet these challenges, this thesis makes the following contributions: First, we study different platforms that are suitable for long-term data recording: A wrist-worn sensor and mobile phones. The latter has shown different carrying behaviours for various users. This has to be considered in ubiquitous systems for accurately recognizing the user’s context. We evaluate our findings in a study with a wrist-worn accelerometer by correlating with the inertial data of a smart phone. Second, we investigate datasets that exhibit rhythmic patterns to be used for recognizing high-level activities. Such statistical information obtained over a population is collected with time use surveys which describe how often certain activities are performed by the user. From such datasets we extract features like time and location to describe which activities are detectable by making use of prior information, showing also the benefits and limits of such data. Third, in order to improve on the recognition rates of high-level activities from wearable sensor data only, we propose the use of the aforementioned prior information from time use data. In our approach we investigate the results of a common classifier for several high-level activities, after which we compare them to the outcome of a maximum-likelihood estimation on the time use survey data. In a last step, we show how these two classification approaches are fused to raise the recognition rates. In a fourth contribution we introduce a recording platform to capture sleep and sleep behaviour in the user’s common environment, enabling the unobtrusive monitoring of patterns over several weeks. We use a wrist-worn sensor to record inertial data from which we extract sleep segments. For this purpose, we present three different sleep detection approaches: A Gaussian-, generative model- and stationary segments-based algorithm are evaluated and are found to exhibit different accuracies for detecting sleep. The latter algorithm is pitted against two clinically evaluated sleep detection approaches, indicating that we are able to reach an optimum trade-off between sleep and wake segments, while the two common algorithms tend to overestimate sleep. Further, we investigate the rhythmic patterns within sleep: We classify sleep postures and detect muscle contractions with a high confidence, enabling physicians to efficiently browse through the data

Predicting Sleeping Behaviors in Long-Term Studies with Wrist-Worn Sensor Data

This paper conducts a preliminary study in which sleeping behavior is predicted using long-term activity data from a wearable sensor. For this purpose, two scenarios are scrutinized: The first predicts sleeping behavior using a day-of-the-week model. In a second scenario typical sleep patterns for either working or weekend days are modeled. In a continuous experiment over 141 days (6 months), sleeping behavior is characterized by four main features: the amount of motion detected by the sensor during sleep, the duration of sleep, and the falling asleep and waking up times. Prediction of these values can be used in behavioral sleep analysis and beyond, as a component in healthcare systems

Rhythm Modelling of Long-Term Activity Data

Long-term monitoring for activity recognition opens up new possibilities for deriving characteristics from the data, such as daily activity rhythms and certain quality measures for the activity performed or for identifying similarities or differences in daily routines. This thesis investigates the detection of activities with wearable sensors and addresses two major challenges in particular: The modelling of a person’s behaviour into rhythmic patterns and the detection of high-level activities, e.g., having lunch or sleeping. To meet these challenges, this thesis makes the following contributions: First, we study different platforms that are suitable for long-term data recording: A wrist-worn sensor and mobile phones. The latter has shown different carrying behaviours for various users. This has to be considered in ubiquitous systems for accurately recognizing the user’s context. We evaluate our findings in a study with a wrist-worn accelerometer by correlating with the inertial data of a smart phone. Second, we investigate datasets that exhibit rhythmic patterns to be used for recognizing high-level activities. Such statistical information obtained over a population is collected with time use surveys which describe how often certain activities are performed by the user. From such datasets we extract features like time and location to describe which activities are detectable by making use of prior information, showing also the benefits and limits of such data. Third, in order to improve on the recognition rates of high-level activities from wearable sensor data only, we propose the use of the aforementioned prior information from time use data. In our approach we investigate the results of a common classifier for several high-level activities, after which we compare them to the outcome of a maximum-likelihood estimation on the time use survey data. In a last step, we show how these two classification approaches are fused to raise the recognition rates. In a fourth contribution we introduce a recording platform to capture sleep and sleep behaviour in the user’s common environment, enabling the unobtrusive monitoring of patterns over several weeks. We use a wrist-worn sensor to record inertial data from which we extract sleep segments. For this purpose, we present three different sleep detection approaches: A Gaussian-, generative model- and stationary segments-based algorithm are evaluated and are found to exhibit different accuracies for detecting sleep. The latter algorithm is pitted against two clinically evaluated sleep detection approaches, indicating that we are able to reach an optimum trade-off between sleep and wake segments, while the two common algorithms tend to overestimate sleep. Further, we investigate the rhythmic patterns within sleep: We classify sleep postures and detect muscle contractions with a high confidence, enabling physicians to efficiently browse through the data

Characterizing Sleeping Trends from Postures

We present an approach to model sleeping trends, using a light-weight setup to be deployed over longer time-spans and with a minimum of maintenance by the user. Instead of characterizing sleep with traditional signals such as EEG and EMG, we propose to use sensor data that is a lot weaker, but also less invasive and that can be deployed unobtrusively for longer periods. By recording wrist-worn accelerometer data during a 4-week-long study, we explore in this poster how sleeping trends can be characterized over long periods of time by using sleeping postures only

EPIFAUNA ASSOCIATA A ULVA RIGIDA IN UNA LAGUNA DEL DELTA DEL PO

The macrofauna associated with the native Chlorophyta Ulva rigida C. Agardh, 1822 has been investigated along a 5-years study period in a lagoon of the Po River Delta (N-W Adriatic Sea). Numerically dominant species were the amphipod Gammarus aequicauda and the non-indigenous mytilid Arcuatula senhousia. Comparison with the fauna associated to the invasive Gracilaria vermiculophylla (Ohmi) Papenfuss, present in the same lagoon, showed that U. rigida had lower richness and diversity

Characterizing Sleeping Trends from Postures

We present an approach to model sleeping trends, using a light-weight setup to be deployed over longer time-spans and with a minimum of maintenance by the user. Instead of characterizing sleep with traditional signals such as EEG and EMG, we propose to use sensor data that is a lot weaker, but also less invasive and that can be deployed unobtrusively for longer periods. By recording wrist-worn accelerometer data during a 4-week-long study, we explore in this poster how sleeping trends can be characterized over long periods of time by using sleeping postures only

How to Log Sleeping Trends? A Case Study on the Long-Term Capturing of User Data

Designing and installing long-term monitoring equipment in the users home sphere often presents challenges in terms of reliability, privacy, and deployment. Taking the logging of sleeping postures as an example, this study examines data from two very different modalities, high-fidelity video footage and logged wrist acceleration, that were chosen for their ease of setting up and deployability for a sustained period. An analysis shows the deployment challenges of both, as well as what can be achieved in terms of detection accuracy and privacy. Finally, we evaluate the benefits that a combination of both modalities would bring.</p