Real time accelerometer-based gait recognition using adaptive windowed wavelet transforms

Wang, Jianhua; Ding, Jian-Jiun; Yu, Chunshui; Chen, Hsin-Hui

doi:10.1109/apccas.2012.6419104

Cited by 30 publications

(14 citation statements)

References 9 publications

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“…Regarding the classification results, performance of the neural network on the proposed event-based dynamic segmentation is higher (>95%) than that obtained with the relevant fixed window sizes. While a number of studies encouraged the use of fixed window size segmentation for activity recognition problem, the results obtained in this work seem to go in the other direction, as these results are better than the ones obtained by Hong et al [6] (93.78%), Lee et al [8] (86-92%, with 10 s of window size) and Wang et al (93.3%) [7]. Fixed window size can thus be sub-optimal when activities last for significantly shorter or longer time periods than the window length, or when activity durations vary over time.…”

Section: Discussioncontrasting

confidence: 55%

“…In the first class of techniques, the signal is divided into consecutive windows of fixed length. In the case of locomotion activities, used lengths lie in the range (1-10) s [4,5,6,7,8], and the presence of overlapping between consecutive windows is usually limited to 50%. One limitation of this approach is that problems can arise if an activity lasts for shorter or longer time periods than the pre-defined window length.…”

Section: Introductionmentioning

confidence: 99%

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Real time event-based segmentation to classify locomotion activities through a single inertial sensor

Fida

Bibbo

Bernabucci

et al. 2015

Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare - "Transforming Healthcare

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We propose an event-based dynamic segmentation technique for the classification of locomotion activities, able to detect the midswing, initial contact and end contact events. This technique is based on the use of a shank-mounted inertial sensor incorporating a tri-axial accelerometer and a tri-axial gyroscope, and it is tested on four different locomotion activities: walking, stair ascent, stair descent and running. Gyroscope data along one component are used to dynamically determine the window size for segmentation, and a number of features are then extracted from these segments. The event-based segmentation technique has been compared against three different fixed window size segmentations, in terms of classification accuracy on two different datasets, and with two different feature sets. The dynamic event-based segmentation showed an improvement in terms of accuracy of around 5% (97% vs. 92% and 92% vs. 87%) and 1-2% (89% vs. 87% and 97% vs. 96%) for the two dataset, respectively, thus confirming the need to incorporate an event-based criterion to increase performance in the classification of motion activities.

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Section: Discussioncontrasting

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Real time event-based segmentation to classify locomotion activities through a single inertial sensor

Fida

Bibbo

Bernabucci

et al. 2015

Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare - "Transforming Healthcare

View full text Add to dashboard Cite

show abstract

“…The frequency domain approaches focus on the frequency content of successive windows of measurements based on short-term Fourier transform (STFT) [ 30 ], FFT [ 31 ], and continuous/discrete wavelet transforms (CWT/DWT) [ 30 , 32 , 33 , 34 ], and can generally achieve high accuracy, but suffer from either resolution issues [ 34 ] or computational overheads [ 35 ]. In [ 31 ], steps are identified by extracting frequency domain features in acceleration data through FFT, and the accuracy of

was achieved.…”

Section: Introductionmentioning

confidence: 99%

A Novel Walking Detection and Step Counting Algorithm Using Unconstrained Smartphones

Kang

Huang

2018

Sensors

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Recently, with the development of artificial intelligence technologies and the popularity of mobile devices, walking detection and step counting have gained much attention since they play an important role in the fields of equipment positioning, saving energy, behavior recognition, etc. In this paper, a novel algorithm is proposed to simultaneously detect walking motion and count steps through unconstrained smartphones in the sense that the smartphone placement is not only arbitrary but also alterable. On account of the periodicity of the walking motion and sensitivity of gyroscopes, the proposed algorithm extracts the frequency domain features from three-dimensional (3D) angular velocities of a smartphone through FFT (fast Fourier transform) and identifies whether its holder is walking or not irrespective of its placement. Furthermore, the corresponding step frequency is recursively updated to evaluate the step count in real time. Extensive experiments are conducted by involving eight subjects and different walking scenarios in a realistic environment. It is shown that the proposed method achieves the precision of 93.76% and recall of 93.65% for walking detection, and its overall performance is significantly better than other well-known methods. Moreover, the accuracy of step counting by the proposed method is 95.74%, and is better than both of the several well-known counterparts and commercial products.

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“…Among the known techniques, the sliding window approach is the most widely employed [ 42 , 43 , 44 , 45 ], being regarded as the best approach for research given its simplicity and stability, and a wide range of window lengths has been used in past studies. Windows as short as 0.5 s and 0.8 s were used to recognize walking, jogging, and going up or down the stairs [ 46 ], whereas a window of 1 s with a decision tree (DT) [ 47 ] was used to classify stationary, walking, running, and biking motion modes. Additionally, with a neural network [ 48 ], a window of 2 s was adopted to classify the motion modes of walking, upstairs and downstairs movement, running, and sitting with varying poses.…”

Section: Introductionmentioning

confidence: 99%

Impact of Sliding Window Length in Indoor Human Motion Modes and Pose Pattern Recognition Based on Smartphone Sensors

Wang

et al. 2018

Sensors

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Human activity recognition (HAR) is essential for understanding people’s habits and behaviors, providing an important data source for precise marketing and research in psychology and sociology. Different approaches have been proposed and applied to HAR. Data segmentation using a sliding window is a basic step during the HAR procedure, wherein the window length directly affects recognition performance. However, the window length is generally randomly selected without systematic study. In this study, we examined the impact of window length on smartphone sensor-based human motion and pose pattern recognition. With data collected from smartphone sensors, we tested a range of window lengths on five popular machine-learning methods: decision tree, support vector machine, K-nearest neighbor, Gaussian naïve Bayesian, and adaptive boosting. From the results, we provide recommendations for choosing the appropriate window length. Results corroborate that the influence of window length on the recognition of motion modes is significant but largely limited to pose pattern recognition. For motion mode recognition, a window length between 2.5–3.5 s can provide an optimal tradeoff between recognition performance and speed. Adaptive boosting outperformed the other methods. For pose pattern recognition, 0.5 s was enough to obtain a satisfactory result. In addition, all of the tested methods performed well.

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Real time accelerometer-based gait recognition using adaptive windowed wavelet transforms

Cited by 30 publications

References 9 publications

Real time event-based segmentation to classify locomotion activities through a single inertial sensor

Real time event-based segmentation to classify locomotion activities through a single inertial sensor

A Novel Walking Detection and Step Counting Algorithm Using Unconstrained Smartphones

Impact of Sliding Window Length in Indoor Human Motion Modes and Pose Pattern Recognition Based on Smartphone Sensors

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