An Integrated Wireless Wearable Sensor System for Posture Recognition and Indoor Localization

• Published in: Sensors (Basel, Switzerland) Vol. 16; no. 11; p. 1825
• Main Authors: Huang, Jian, Yu, Xiaoqiang, Wang, Yuan, Xiao, Xiling
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 31.10.2016; MDPI
• Subjects: Algorithms; Attitudes; Data integration; Error correction; Geographic Information Systems; Humans; indoor localization; Joints - physiology; Kalman filters; Knee; Localization; Monitoring, Ambulatory - instrumentation; Monitoring, Ambulatory - methods; Multisensor fusion; Older people; Pitch (inclination); Posture; posture recognition; Radio frequency identification; Radio Frequency Identification Device; Recognition; Segments; Sensors; set-membership filter; Thigh; Walking - physiology; Wearable technology; wireless sensor system; Wireless Technology; wireless sensor system; indoor localization; posture recognition; set-membership filter
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s16111825

In order to provide better monitoring for the elderly or patients, we developed an integrated wireless wearable sensor system that can realize posture recognition and indoor localization in real time. Five designed sensor nodes which are respectively fixed on lower limbs and a standard Kalman filter are used to acquire basic attitude data. After the attitude angles of five body segments (two thighs, two shanks and the waist) are obtained, the pitch angles of the left thigh and waist are used to realize posture recognition. Based on all these attitude angles of body segments, we can also calculate the coordinates of six lower limb joints (two hip joints, two knee joints and two ankle joints). Then, a novel relative localization algorithm based on step length is proposed to realize the indoor localization of the user. Several sparsely distributed active Radio Frequency Identification (RFID) tags are used to correct the accumulative error in the relative localization algorithm and a set-membership filter is applied to realize the data fusion. The experimental results verify the effectiveness of the proposed algorithms.