Conference Paper

Object-Based Activity Recognition with Heterogeneous Sensors on Wrist.

DOI: 10.1007/978-3-642-12654-3_15 Conference: Pervasive Computing, 8th International Conference, Pervasive 2010, Helsinki, Finland, May 17-20, 2010. Proceedings
Source: DBLP

ABSTRACT This paper describes how we recognize activities of daily living (ADLs) with our designed sensor device, which is equipped
with heterogeneous sensors such as a camera, a microphone, and an accelerometer and attached to a user’s wrist. Specifically,
capturing a space around the user’s hand by employing the camera on the wrist mounted device enables us to recognize ADLs
that involve the manual use of objects such as making tea or coffee and watering plant. Existing wearable sensor devices equipped
only with a microphone and an accelerometer cannot recognize these ADLs without object embedded sensors. We also propose an
ADL recognition method that takes privacy issues into account because the camera and microphone can capture aspects of a user’s
private life. We confirmed experimentally that the incorporation of a camera could significantly improve the accuracy of ADL
recognition.

Download full-text

Full-text

Available from: Yasushi Sakurai, Aug 09, 2015
0 Followers
 · 
85 Views
  • Source
    • "Other approaches are based on the assumption that every object is able to provide its state with binary switches [14] or infrared sensors [12]. In [13] a similar approach to our idea is presented. This system uses also a wrist-worn camera in combination with other bodyworn sensor systems. "
    [Show abstract] [Hide abstract]
    ABSTRACT: We address a specific, particularly difficult class of activity recognition problems defined by (1) subtle, and hardly discriminative hand motions such as a short press or pull, (2) large, ill defined NULL class (any other hand motion a person may express during normal life), and (3) difficulty of collecting sufficient training data, that generalizes well from one to multiple users. In essence we intend to spot activities such as opening a cupboard, pressing a button, or taking an object from a shelve in a large data stream that contains typical every day activity. We focus on body-worn sensors without instrumenting objects, we exploit available infrastructure information, and we perform a one-to-many-users training scheme for minimal training effort. We demonstrate that a state of the art motion sensors based approach performs poorly under such conditions (Equal Error Rate of 18% in our experiments). We present and evaluate a new multi modal system based on a combination of indoor location with a wrist mounted proximity sensor, camera and inertial sensor that raises the EER to 79%.
    10th Percom Workshop on Context Modeling and Reasoning (CoMoRea) 2013. San Diego, USA. IEEE; 01/2013
  • Source
    • "The energy can be used to distinguish low intensity activities such as standing from high intensity activities such as walking [17] [1]. The dominant frequency is the frequency that has the largest FFT component, and it allows us to distinguish between repetitive motions with similar energy values [8]. We construct a feature vector concatenating the above features extracted from all the body-worn accelerometers. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes an activity recognition method that models an end user’s activities without using any labeled/ unlabeled acceleration sensor data obtained from the user. Our method employs information about the end user’s physical characteristics such as height and gender to find other users whose sensor data prepared in advance may be similar to those of the end user. Then, we model the end user’s activities by using the labeled sensor data from the similar users. Therefore, our method does not require the end user to collect and label her training sensor data. We confirmed the effectiveness of our method by using 100 hours of sensor data obtained from 40 participants, and achieved a good recognition accuracy almost identical to that of a recognition method employing an end user’s labeled training data.
    Proceedings ISWC'11; 01/2011
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The availability of multiple sensors on mobile devices offers a significant new capability to enable rich user and context aware applications. Many of these applications run in the background to continuously sense user context. However, running these applications on mobile devices can impose a significant stress on the battery life and the use of supplementary low-power processors has been proposed on mobile devices for continuous background activities. In this paper, we experimentally and analytically investigate the design considerations that arise in the efficient use of the low power processor and provide a thorough understanding of the problem space. We answer fundamental questions such as which segments of the application are most efficient to be hosted on the low power processor, and how to select an appropriate low power processor. We discuss our measurements, analysis, and results using multiple low power processors and existing phone platforms.
Show more