With the rise of interest in watch-type and wristband-type devices, wrist-worn devices have become a growing market in the field of wearable activity tracking devices. However, wrist-worn devices have clear disadvantages compared to existing waist-worn and ankle-worn devices including noise from various sensing conditions that cause irregular arm movements. This paper proposes two activity tracking algorithm for wrist-worn devices which overcome the suggested problems of wrist-worn devices. In chapter 1, I propose a step detection algorithm using three-axis accelerometer for wrist-worn devices. The algorithm consists of three phases, which address the problems of wrist-worn devices. The first data preprocessing phase calculates the Euclidean norm of the acceleration vector. It enables the algorithm to track the movement of a device only with the acceleration data. The second data filtering phase reduces the noise with a simple digital low-pass filter. Then, the third peak detection phase adopts a sign-of-slope method and average threshold method to accurately detect the step peaks under different sensor-carrying modes and speed conditions. A wrist-worn hardware prototype is designed and realized for algorithm evaluation. The experiment results show that the proposed algorithm is superior to the compared existing algorithm and commercial devices. The averaged detection error is approximately 1% in different test conditions. In chapter 2, I propose a robust walking speed estimation method with data from a six-axis inertial measurement unit (IMU), which is commonly mounted in wrist-worn devices, and user’s height information. The proposed method provides accurate walking speed estimation results under different sensor-carrying modes and walking speeds. The estimation is based on sensor-carrying mode detection with the TreeBagger model, and on Gaussian process regression (GPR) models, which are adapted to seven predetermined sensor-carrying modes. The speed estimation is done by calculating a weighted sum of multiple GPR models with the probabilities from TreeBagger model. To evaluate the superiority of my method, I implement it on a hardware. An experimental evaluation is performed on 16 healthy subjects with a treadmill. The experimental results show that the proposed method outperforms existing studies and comparable commercial devices for all sensing conditions. The averaged error of the proposed method is about 3% for all sensing conditions, while others show error of more than 15% in different sensing conditions. The results shows that proposed method has novelty compared to existing studies in terms of estimating walking speed accurately under changing sensing conditions only with single IMU sensor.
최근 웨어러블 장치 기술이 발전하면서,스마트 워치, 스마트 밴드와 같은 손목 착용 기기에 대한 수요와 시장의 관심이 늘어나고 있다. 하지만 손목 착용 기기는 착용의 편의성과 정보 확인의 용이성에도 불구하고, 착용자의 움직임에 측정 정확도가 크게 영향을 받고, 기기 자체의 전력과 계산 능력 문제로 다양한 센서를 탑재하기 어렵다는 문제를 가지고 있다. 본 논문에서는 기존 손목 착용 기기들의 문제를 해결하여, 다양한 걸음 자세 하에서도 단일 관성측정유닛 데이터만을 이용하여 착용자의 걸음 수와 걸음 속도를 정확하게 측정하는 방법을 다루었다. 걸음 수 측정과 속도 측정은 손목 착용 기기의 가속도 센서 값과 자이로스코프 센서 값만을 이용하여 이루어 졌으며, 제안된 방식은 실험 결과 기존에 존재하던 방식과 상용 손목 착용 기기들과 비교하였을 때 더 나은 성능을 보여주었고, 특히 주머니에 손 넣고 걷기, 전화하면서 걷기와 같은 평범하지 않은 상황에서 그 차이가 크게 벌어짐을 확인할 수 있었다.
