A study on automatic recognition of object use exploiting motion correlation of wireless sensors

Abstract

An essential component in the ubiquitous computing vision is the ability of detecting with which objects the user is interacting during his or her activities. We explore in this paper a solution to this problem based on wireless motion and orientation sensors (accelerometer and compass) worn by the user and attached to objects. We evaluate the performance in realistic conditions, characterized by limited hardware resources, measurement noise due to motion artifacts and unreliable wireless communication. We describe the complete solution, from the theoretical design, going through simulation and tuning, to the full implementation and testing on wireless sensor nodes. The implementation on sensor nodes is lightweight, with low communication bandwidth and processing needs. Compared to existing work, our approach achieves better performance (higher detection accuracy and faster response times), while being much more computationally efficient. The potential of the concept is further illustrated by means of an interactive multi-user game. We also provide a thorough discussion of the advantages, limitations and trade-offs of the proposed solution.

A Motion Similarity Measurement Method of Two Mobile Devices for Safety Hook Fastening State Recognition

“…The problem we are focusing on in this study is similar to those in existing studies in which attempts were made to classify whether two sensors are located in the same place (person) [20]- [23]. However, there are major differences with the existing studies and ours in which the mobile devices are attached to a part of the body.…”

“…They utilized the rotation information to de-rotate the orientation of two mobile devices yet they did not consider the similarity of motion of the rotational direction. Bosch et al [23] measured the similarity of motions by calculating the correlation of linear motion using accelerometers and the rotary motion using compass sensors. The correlation of the linear motion and the rotary motion was calculated independently, and the weights that were determined heuristically were multiplied to each correlation, and combined.…”

“…Therefore, measuring the similarity of the rotary motion with the magnitude of acceleration can be effective for the fastening state recognition. The similarity of rotary motion has been considered in [23], but heuristically determined weights were required to combine the similarity of the magnitude of acceleration with the similarity of the rotary motion. It is expected that a training dataset is needed to tune parameters properly for other applications [22].…”

“…Although it is possible to represent the data as an ordinal variable, when the data are represented as a continuous variable, it showed a comparatively better performance [21]. In particular, Pearson correlation coefficient (PCC) that measures the linear correlation between two sets of continuous data has been commonly used in the studies [21], [23] as a similarity measurement method. In order to measure the linear correlation through PCC effectively, there exists a prerequisite that the data should have a normal distribution.…”

Optimal Sensor Placement for Measuring Physical Activity with a 3D Accelerometer

“…The ProMove2 (65 × 50 × 30 mm, 70 g; which is used in this study, is the developer model of the ProMove3D containing the same 3D MEMS inertial sensor, see Figure 1 . The ProMove 2 and its successive model the ProMove3D have already been implemented in a number of telemonitoring studies [ 25 , 27 – 29 ].…”