Human activity recognition method based on inertial sensor and barometer

Xie, Lili; Tian, Junfang; Ding, Genming; Zhao, Qian

doi:10.1109/isiss.2018.8358140

Cited by 27 publications

(23 citation statements)

References 11 publications

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“…Indeed, for the data collected while using an elevator, the maximum derivative was 0.2 millibar/s, that was three times higher than the maximum derivative obtained during running on the stairs. The high performance achieved in detecting stairs corroborated the validity of previous studies involving barometer data [17,19]. In addition, as shown in Figure 5, the time derivative of the barometric pressure varies very slowly across different altitudes (it is substantially constant in the range 0 to 3000 m and in particular around 0.028 millibar/s, which is the average variation in the training set), thus it is not necessary to re-calibrate the values of td L and td U , when employing the algorithm at altitudes relevant for older people.…”

Section: Discussionsupporting

confidence: 81%

“…In the context of HAR for detection of stair climbing, approaches mainly differ for the sensing modality, position of the sensor/s, features, algorithms used for classification, and target users. Sensors comprise IMUs [5,[12][13][14][15] and their combined use with barometers [16][17][18][19][20][21]. Positions include wrist [16,17], chest [16,18,20], waist [5,12,17,19], calf and foot [13][14][15][16]21].…”

Section: Introductionmentioning

confidence: 99%

“…Sensors comprise IMUs [5,[12][13][14][15] and their combined use with barometers [16][17][18][19][20][21]. Positions include wrist [16,17], chest [16,18,20], waist [5,12,17,19], calf and foot [13][14][15][16]21]. Common features are averages, ranges, Fourier Transform and Wavelet coefficients, statistical moments of sensor data, whereas classifiers are Support Vector Machine (SVM) [5,12,16,17,19], K-Nearest Neighbour (KNN) [5,16,17], Decision Tree and Random Forest [5,13,17,19], Artificial Neural Network (ANN) [22], and also convolutional NN [15].…”

Section: Introductionmentioning

confidence: 99%

“…Positions include wrist [16,17], chest [16,18,20], waist [5,12,17,19], calf and foot [13][14][15][16]21]. Common features are averages, ranges, Fourier Transform and Wavelet coefficients, statistical moments of sensor data, whereas classifiers are Support Vector Machine (SVM) [5,12,16,17,19], K-Nearest Neighbour (KNN) [5,16,17], Decision Tree and Random Forest [5,13,17,19], Artificial Neural Network (ANN) [22], and also convolutional NN [15]. Users span from healthy young people up to elderly patients with severe conditions such as Parkinson's disease or stroke [5,16,20].…”

Section: Introductionmentioning

confidence: 99%

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Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting

et al. 2020

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Wearable sensors play a significant role for monitoring the functional ability of the elderly and in general, promoting active ageing. One of the relevant variables to be tracked is the number of stair steps (single stair steps) performed daily, which is more challenging than counting flight of stairs and detecting stair climbing. In this study, we proposed a minimal complexity algorithm composed of a hierarchical classifier and a linear model to estimate the number of stair steps performed during everyday activities. The algorithm was calibrated on accelerometer and barometer recordings measured using a sensor platform worn at the wrist from 20 healthy subjects. It was then tested on 10 older people, specifically enrolled for the study. The algorithm was then compared with other three state-of-the-art methods, which used the accelerometer, the barometer or both. The experiments showed the good performance of our algorithm (stair step counting error: 13.8%), comparable with the best state-of-the-art (p > 0.05), but using a lower computational load and model complexity. Finally, the algorithm was successfully implemented in a low-power smartwatch prototype with a memory footprint of about 4 kB.

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Section: Discussionsupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting

et al. 2020

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“…As a remark, the authors of [64] mentioned the usage of time-and frequency-domain features, but they did not specify which ones exactly. [29,45,48-50,53,55,56,59,61-63, 65,67-69,71,72,75,76,78,80-82, 89-91] Pairwise Correlation Correlation between every pair of axes [50,53,55,56,[60][61][62]65,69,78,80,82,89,91] Minimum Smallest value in the window [45,46,57,69,72,76,82,90,91] Maximun Largest value in the window [45,46,57,69,72,76,82,90,91] Energy Average sum of squares [49,50,71,75,76,78,82,91] Signal Magnitude Area [47,50,53,78,80,…”

Section: Shallow Methodsmentioning

confidence: 99%

Human Activity Recognition for Production and Logistics—A Systematic Literature Review

et al. 2019

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This contribution provides a systematic literature review of Human Activity Recognition for Production and Logistics. An initial list of 1243 publications that complies with predefined Inclusion Criteria was surveyed by three reviewers. Fifty-two publications that comply with the Content Criteria were analysed regarding the observed activities, sensor attachment, utilised datasets, sensor technology and the applied methods of HAR. This review is focused on applications that use marker-based Motion Capturing or Inertial Measurement Units. The analysed methods can be deployed in industrial application of Production and Logistics or transferred from related domains into this field. The findings provide an overview of the specifications of state-of-the-art HAR approaches, statistical pattern recognition and deep architectures and they outline a future road map for further research from a practitioner's perspective.

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Segmentation and Recognition of Basic and Transitional Activities for Continuous Physical Human Activity

Tian

et al. 2019

IEEE Access

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Human activity recognition (HAR) is a hot research topic which aims to understand human behavior and can be applied in various applications. However, transitions between activities are usually disregarded due to their low incidence and short duration when compared against other activities, while in fact, transitions can affect the performance of the recognition system if not dealt with properly. In this paper, we propose and implement a systematic human activity recognition method to recognize basic activities (BA) and transitional activities (TA) in a continuous sensor data stream. First, raw sensor data are segmented into fragments with sliding window and the features are constructed based on window segmentation. Then, cluster analysis with K-Means is used to aggregate activity fragments into periods. Next, generally, realize the classification of BA and TA according to the shortest duration of the BA, and then deal with the hidden phenomenon of BA. Third, the fragments between adjacent BA are evaluated to decide whether they are TA or disturbance process. Finally, random forest classifier is used to accurately recognize BA and TA. The proposed method is evaluated on the public dataset SBHAR. The results demonstrate that our method effectively recognizes different activities and can deliver high accuracy with all activities considered. INDEX TERMS Human activity recognition, transitional activities, period extraction, cluster analysis. I. INTRODUCTIONHuman activity recognition (HAR) detects, interprets and identifies human behavior, activity types and patterns, either habitual or occasional. With its potential applications spanning various areas such as smart home, healthcare, nursing, and athletics, it could aid transforming our daily life to be smarter, safer and more convenient [1]-[4].HAR methods can be divided into two main categories: vision-based and sensor-based [5]-[10]. However, many limitations exist in vision-based methods, especially when applied in practical scenarios. For example, the use of cameras is constrained by various factors such as illumination, location, angle, potential obstruction, and privacy invasion concerns, which are far from ideal in many scenarios [8], [9].The associate editor coordinating the review of this manuscript and approving it for publication was Dong Wang.

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Human activity recognition method based on inertial sensor and barometer

Cited by 27 publications

References 11 publications

Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting

Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting

Human Activity Recognition for Production and Logistics—A Systematic Literature Review

Segmentation and Recognition of Basic and Transitional Activities for Continuous Physical Human Activity

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