PhD Candidate Signal Processing Systems Group, Dept. of Electrical Engineering, Eindhoven University of Technology, the Netherlands Brain, Body & Behavior Group, Philips Research Europe
MSc Dept. of Electrical Engineering, Eindhoven University of Technology, the Netherlands
BEng Dept. of Information Science and Electronic Engineering, Zhejiang University, China
Abstract: The growing number of people adopting a sedentary lifestyle these days creates a serious need for effective physical activity promotion programs. Often, these programs monitor activity, provide feedback about activity and offer coaching to increase activity. Some programs rely on a human coach who creates an activity goal that is tailored to the characteristics of a participant. Throughout the program, the coach motivates the participant to reach his personal goal or adapt the goal, if needed. Both the timing and the content of the coaching are important for the coaching. Insights on the near future state on, for instance, behaviour and motivation of a participant can be helpful to realize an effective proactive coaching style that is personalized in terms of timing and content. As a first step towards providing these insights to a coach, this chapter discusses results of a study on predicting daily physical activity level (PAL) data from past data of participants in a lifestyle intervention program. A mobile body-worn activity monitor with a built-in triaxial accelerometer was used to record PAL data of a participant for a period of 13 weeks. Predicting future PAL data for all days in a given period was done by employing autoregressive integrated moving average (ARIMA) models on the PAL data from days in the period before. By using a newly proposed categorized-ARIMA (CARIMA) prediction method, we achieved a large reduction in computation time without a significant loss in prediction accuracy in comparison with traditional ARIMA models. In CARIMA, PAL data are categorized as stationary, trend or seasonal data by assessing their autocorrelation functions. Then, an ARIMA model that is most appropriate to these three categories is automatically selected based on an objective penalty function criterion. The results show that our CARIMA method performs well in terms of PAL prediction accuracy (~9% mean absolute percentage error), model parsimony and robustness.
Abstract: Results are provided on predicting daily physical activity level (PAL) data from past data of participants of a physical activity lifestyle program aimed at promoting a healthier lifestyle consisting of more physical exercise. The PAL data quantifies the level of a personâs daily physical activity and reflects the daily energy expenditure of this person. In this wellbeing program, a mobile body-worn activity monitor with a built-in triaxial accelerometer was used to record the PAL data of an individual for a period of 13 weeks. The autoregressive integrated moving average (ARIMA) models were employed to predict future PAL data of every next week. This paper proposes a categorized-ARIMA (CARIMA) prediction method which achieves a large reduction in computation time without significant loss in prediction accuracy compared with the traditional ARIMA. In the current method, PAL data were categorized as being stationary, trend or seasonal via assessing their autocorrelation functions. The most appropriate ARIMA model for these three categories was automatically selected by applying the objective penalty function criterion. The results show that our CARIMA method performed well in terms of PAL prediction accuracy (~9% mean absolute percentage error), model parsimony and robustness.
Abstract: In this study, a single tri-axial accelerometer placed on the waist was used to record the acceleration data for human physical activity classification. The data collection involved 24 subjects performing daily real-life activities in a naturalistic environment without researchers' intervention. For the purpose of assessing customers' daily energy expenditure, walking, running, cycling, driving, and sports were chosen as target activities for classification. This study compared a Bayesian classification with that of a Decision Tree based approach. A Bayes classifier has the advantage to be more extensible, requiring little effort in classifier retraining and software update upon further expansion or modification of the target activities. Principal components analysis was applied to remove the correlation among features and to reduce the feature vector dimension. Experiments using leave-one-subject-out and 10-fold cross validation protocols revealed a classification accuracy of ~80%, which was comparable with that obtained by a Decision Tree classifier.
Abstract: As a code technology of the fourth generation in the future, Orthogonal Frequency Division Multiplexing (OFDM) is a type of high-data-rate wireless transmission method with high focus nowadays. By combining the advantages of OFDM and MIMO technologies at transmitter and receiver, the system can increase data throughput and enhance system capacity on time-varying and frequency-selective (fading and multipath) channels without increasing transmission power and enlarging bandwidth. Synchronization technique is very important for any digital communication system. OFDM system is a Multi Carrier Modulation (MCM) scheme and a receiver for MIMO-OFDM systems requires exact time and frequency synchronization for subcarriers so as to improve performance. Frame detection is one of the key tasks in synchronization implementation because it is to find the start position of received signal. Chapter 1 of this thesis mainly introduces the background and research situation all around the world of MIMO-OFDM transceiver system based on IEEE 802.11a standard and hardware realization of the receiver system. Chapter 2 briefly introduces the synchronization approach of receiver. Chapter 3 provides an analysis report of different frame detection algorithms, and then an improved algorithm will be described. The performance of different algorithms is compared implemented by using MATLAB in Chapter 4. The the simulations are spread to 2Ã2 and 4Ã4 antennas structure. Chapter 5 aims at achieving Verilog HDL simulation and hardware implementation of FPGA for the best frame detection algorithem which is given in Chapter 4. The last chapter is to sum up the key points mentioned above and show the future work.
