2015 IEEE International Symposium on Circuits and Systems (ISCAS) 2015
DOI: 10.1109/iscas.2015.7168746
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Real-time arm movement recognition using FPGA

Abstract: In this paper we present a FPGA-based system to detect three elementary arm movements in real-time (reach and retrieve, lift cup to mouth, rotation of the arm) using data from a wrist-worn accelerometer. Recognition is carried out by accurately mapping transitions of predefined, standard orientations of an accelerometer to the corresponding arm movements. The algorithm is coded in HDL and synthesized on the Altera DE2-115 FPGA board. For real-time operation, interfacing between the streaming sensor unit, host … Show more

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Cited by 6 publications
(5 citation statements)
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“…Similarly, Zhang et al [ 80 ] have used accelerometers and gyroscopes communicating with a smartphone using Bluetooth. Biswas et al [ 81 ] have used an accelerometer on the dominant wrist of a subject to perform arm movement recognition. The data is sent from the accelerometer to a local computer, which then transfers it to a FPGA board using a RS232 cable.…”
Section: Real-time Centralized Activity Recognitionmentioning
confidence: 99%
See 1 more Smart Citation
“…Similarly, Zhang et al [ 80 ] have used accelerometers and gyroscopes communicating with a smartphone using Bluetooth. Biswas et al [ 81 ] have used an accelerometer on the dominant wrist of a subject to perform arm movement recognition. The data is sent from the accelerometer to a local computer, which then transfers it to a FPGA board using a RS232 cable.…”
Section: Real-time Centralized Activity Recognitionmentioning
confidence: 99%
“…Nugyen et al [ 54 ] have used binary rules that map sensor states to an activity label to classify new instances in near real-time (5 min time slices). Other straightforward approaches use real-time threshold based classification [ 92 ] or a mapping between gyroscope orientation and activities [ 81 ].…”
Section: Real-time Centralized Activity Recognitionmentioning
confidence: 99%
“…Many smartphones based HAR systems are available [32], but a very limited work has been reported on dedicated hardware (FPGA) based HAR implementation. Like in [30], Biswas et al designed and implemented the real-time arm movement recognition that required 41.2 microseconds for recognition. Yan et al [29] tested the MLP based HAR design on the two different FPGAs and got impressive results compared to the smartphones implementations.…”
Section: Related Workmentioning
confidence: 99%
“…Hence, the hardware is a favorable choice for HAR implementation for the challenging applications. Few attempts of HAR algorithm implementation on reconfigurable hardware are reported in the literature [22], [29], [30]. These implementations are focused on the hardware design and modeling of activity classifier.…”
Section: Introductionmentioning
confidence: 99%
“…Apart from reducing communication (through on-node data processing and advocating light-weight algorithms), the focus has been on issues such as deactivation of power hungry sensors [29] (e.g. gyroscopes) and adaptive sampling rate [30]. Hence, to the best of our knowledge, this is the first work which has focused on an optimized, low-complexity algorithm-to-architecture mapping aimed towards a hardware/accelerator based design to be used within resource constrained senor nodes.…”
Section: Related Workmentioning
confidence: 99%