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

Abstract

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.

Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain–Machine Interface

“…Moreover, this study only used the TMAGRL for three classifications; the study about using TMAGRL for more classifications or continuous neural decoding can be explored. Additionally, learning from other RL system [ 52 ] or using a wearable sensor system [ 53 , 54 ] to determine the reward of the RL system may further improve the practicability. In the future, we intend to test our method integrated with a wireless wearable sensor system on a brain control task to further verify its effectiveness in clinical application.…”

UWB Monitoring System for AAL Applications

“…There are also several works involving RFID-based indoor localization. Huang et al [ 14 ] presented an approach that combines wearable MEMS sensors with active RFID tags serving as beacons deployed in the vital positions of the area covered by the localization system. MEMS sensors are attached to the person at their lower limbs (two per leg) and waist (one sensor), which allows for estimation of the posture (which can be distinguished as sitting, standing, squatting, supine and prone) and gait-based position calculation.…”

“…Wang et al [ 27 ] have presented a fall-detection system based on the wearable MEMS sensors integrated with the active RFID technology used for easy localization of the patient in the case of falling down. Huang et al have proposed similar solution in [ 14 ].…”

A Posture Recognition Method Based on Indoor Positioning Technology

“…The competition has witnessed lots of positioning technologies including wireless local area networks (WLAN) [19], Bluetooth low energy [20], optical light [21], radio frequency identification (RFID) [22], and UWB [23,24]. Among these methods, UWB is considered to be one of the most accurate approaches because it provides positioning estimation with centimeter-level accuracy [25,26].…”