Efficient Sitting Posture Recognition for Wheelchair Users: An Unsupervised Data-Driven Framework
Item Type Article
Authors Kini, K. Ramakrishna; Harrou, Fouzi; Madakyaru, Muddu; Kadri, Farid; Sun, Ying
Citation Kini, K. R., Harrou, F., Madakyaru, M., Kadri, F., & Sun, Y. (2023).
Efficient Sitting Posture Recognition for Wheelchair Users: An Unsupervised Data-Driven Framework. IEEE Instrumentation &
Measurement Magazine, 26(4), 37–43. https://doi.org/10.1109/
mim.2023.10146559 Eprint version Post-print
DOI 10.1109/mim.2023.10146559
Publisher Institute of Electrical and Electronics Engineers (IEEE) Journal IEEE Instrumentation & Measurement Magazine
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Efficient Sitting Posture Recognition for Wheelchair Users:
An Unsupervised Data-Driven Framework
K. Ramakrishna Kinia, Fouzi Harroub (IEEE Senior Member), Muddu Madakyaru c* (Corresponding Author), Farid Kadrid, Ying Sunb
a Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India. Email: [email protected]
b King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, 23955-6900, Saudi Arabia, e-
mail:[email protected]; [email protected]
c* Department of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India. Email: [email protected]
dAeroline DATA & CET, Agence 1031, Sopra Steria Group, Colomiers, 31770, France.
Email: [email protected]
Introduction
Automatic and reliable detection of a person’s posture when sitting in a wheelchair is necessary to prevent major health issues. This study introduces an unsupervised anomaly detection and isolation approach to automatically recognize unbalanced sitting posture in a wheelchair using data from pressure sensors embedded in the wheelchair. Importantly, the advantages of independent component analysis (ICA) will be integrated with those of Kantorovich Distance (KD)-driven anomaly detector by developing an ICA-driven KD methodology that can handle non-Gaussianity in the data ameliorates the quality of anomaly detection. Due to pressure data displaying a non-Gaussian behavior, this work adopts ICA, which is well suited to handle this type of data. At the same time, the KD scheme is an effective anomaly detection indicator to evaluate the ICA residuals. Furthermore, the contribution plot strategy, which does not need a priori knowledge of anomalies, is employed for discriminating the type of the detected abnormal posture if it is caused due to higher pressure on the right side, on the left side, or higher forward pressure. The ICA-KD approach only employs normal events data to train the detection model, making them more attractive for identifying a person’s posture in practice.
The overall detection system provided a promising performance with an F1-score around 99.41%, outperforming some commonly used monitoring methods.
The number of wheelchair users increases year by year due to different factors, including traffic accidents, falls, and violence. According to the WHO World Report on Disability and wheelchair foundation, approximately 1.85% of people worldwide need a wheelchair. For instance, there are 3.3 million wheelchair users in the US, and the number is increasing with an expected 2 million new wheelchair users every year. In Europe, the number of wheelchair users is around 5 million people representing 1% of the population [1]. Wheelchairs are designed to improve users' quality of life by facilitating mobility, social interaction, and occupation.
However, unbalanced sitting posture in a wheelchair can negatively affect the health condition of wheelchair users. It is worth noting that even healthy people sitting in front of a computer all day can be affected by sitting posture. Specifically, incorrect posture in a wheelchair causes chronic pain, sclerosis, kyphosis, skin and respiratory problems, loss of brain skills, and physical health problems, such as muscle rigidity, fatigue, and muscle pain [2]. Alternatively, adequate sitting in the wheelchair can decrease pain intensity and the possibility of ulcers
formation [3]. Thus, accurately detecting unbalanced sitting posture in a wheelchair provides relevant information to the wheelchair user to avoid improper postures.
In recent years, increased attention to sitting posture recognition resulted in the development of various technologies in terms of the adopted sensors, including wearable sensors, visual sensors, and non-intrusive pressure sensors [6,7]. In [8], wearable optical fiber sensors have been adopted to monitor the user’s sitting posture. However, the use of wearable sensors by wheelchair users is not comfortable and very intrusive. Other researchers utilize information from images and videos collected via visual sensors to recognize the sitting posture [9].
However, the vision-based solution can seriously be impacted by the lighting level. In addition, this solution could be limited by privacy concerns for people being monitored. Alternatively, non-intrusive approaches are generally based on data gathered from pressure sensors embedded in the wheelchair. Such approaches do not require human intervention and no wearing of sensors, making them very suitable for practical application [5].
Accurate recognition of sitting posture is vital to enhancing wheelchair users' comfort and health monitoring. Various non-intrusive methods have been introduced in the literature for sitting posture recognition. For instance, in [4], a supervised approach using principal component analysis (PCA) as a feature extractor and the k-nearest neighbors (KNN) classifier for posture recognition in conventional chairs. This approach reached a classification accuracy of 75%. This is because PCA is suitable only for Gaussian data; however, the data collected from the sensors embedded in the chair are non-gaussian, which leads to miss-classification results. Recently in [5], a supervised machine learning approach has been introduced to identify a person's posture based on a sensors network embedded in the wheelchair. This approach used the Condensed Nearest-Neighbors approach for data filtering, the Kennard-Stone algorithm to balance the data, PCA for dimensionality reduction, and K-nearest neighbors algorithm for posture classification. Results indicated that this combined approach achieved an average accuracy of over 75%. This could be due to the small-sized and unbalanced data used in this study. Most of the developed detection schemes for sitting posture recognition are generally designed using shallow supervised techniques that need labeled data in training. However, getting labeled data is not obvious and is time-consuming. Thus, this study aims to design a semi-supervised data-driven detector for sitting posture monitoring that does not require labeled data.
This paper presents a data-driven approach for detecting and identifying wheelchair users’
posture using unlabeled data. Also, this study intends to study the detection capacity of the independent component analysis (ICA)-based monitoring approach on datasets of limited size.
Specifically, the proposed approach combines the advantages of the ICA model and KD-based monitoring chart to obtain good detection. Unlike PCA employing orthogonal principal components, ICA utilizes a linear non-orthogonal coordinate system, where the directions depend on higher-order statistics to handle non-Gaussianity in the data. The KD monitoring chart is applied to the residuals generated from the ICA for abnormal event detection. The employment of the KD-based chart is expected to improve the detection of the ICA-based approach. Once the ICA-KD approach detects the abnormal posture, a contribution plot is conducted to identify the type of sitting posture. Experiments based on a public dataset provided in [5] demonstrate that the proposed ICA-KD approach can effectively reduce the false alarm rate and reach a 99% detection accuracy.
The remainder of this paper is organized as follows. The following section briefly describes the preliminary materials, including the ICA and the KD anomaly detector. Then, the coupled ICA-
KD technique that identifies the sitting position in a wheelchair is presented. After that, we assessed the performance of the proposed approach using a publicly available dataset. Finally, we offer conclusions to this study
A data-driven wheelchair user’s posture detection and isolation strategy
The sitting posture monitoring approach is performed in four steps: empirical model construction using anomaly-free data, residual generation using the constructed model, posture sitting detection, and isolation (Figure 1).
Fig.1. Schematic of the proposed strategy.
Methodology:
Independent Component Analysis-based monitoring approach
ICA is a data-driven technique that considers statistical parameters of the higher order for extracting important non-gaussian features from the process data. Once the data 𝐗 = [𝐱𝟏, 𝐱𝟐, . . . 𝐱𝐧]𝐓 with 𝐗 ∈ 𝐑𝐦 ∗ 𝐧 is available, the ICA model may be represented as [12]:
X = AS + F (1) where A, S and F represent the mixing matrix, the matrix with independent components (ICs)
and the residual matrix respectively. Since there are two unknown entities in equation (1), the aim of ICA is to find a separating matrix W which can be computed from 𝐒̂ as follows:
𝐒̂ = 𝐖𝐗 (2) The initial step in ICA involves a normalization process where the measurements in data X are scaled to have zero mean. Next, the whitening step is carried out to remove cross-correlation between the variables, and the whitening transformation is represented using the following expression:
Z = QX (3)
where Z denotes the whitening matrix, 𝐐 = 𝚲−𝟏𝐔𝐓, 𝚲 refers to the diagonal matrix and U is the eigenvector matrix computed from covariance of X. From equation (1), it is known that X=AS. Using this, the equation (3) can be expressed as:
Z = QX = QAS = VS (4) Where V is an orthogonal matrix which is easy to be estimated since it has very few parameters.
From equation (4), S can be determined as follows:
𝐒̂ = 𝐕𝐓𝐙 = 𝐕𝐓𝐐𝐗 (5)
It results from equations (2) and (5) that a relationship between W and V can be expressed: 𝐖 = 𝐕𝐓 𝐐 (6)
The next task is to estimate the ICs, and this is possible by utilizing a fixed-point algorithm that is based on maximizing non-gaussianity using negentropy approximation [12]. Once the matrix of ICs is determined, the next task is to select the k dominant ICs and this is achieved using the cumulative percentage variance (CPV) technique. The developed ICA model is made up of systematic space that represents a model having k dominant ICs, excluded space having m-k ignored ICs and the residual space. The ICA-based anomaly detection strategy consists of anomaly (abnormal event) indicators that can monitor three parts of ICA model and they can be represented in the following manner: 𝐼𝑑2 = 𝐗𝐓𝐖𝐤𝐓𝐖𝐤𝐗 (7)
𝐼𝑒2 = 𝐗𝐓𝐖𝐦−𝐤𝐓 𝐖𝐦−𝐤𝐗 (8)
𝑆𝑃𝐸 = 𝐞𝐞𝐓 (9)
where, 𝐞 = 𝐗 − 𝐗̂ (10)
When new data is available, these indicators are computed and compared with the reference threshold to check for the abnormal events. The reference threshold is computed using kernel density estimation (KDE) technique [12]. The abnormal event indicators provide an efficient way of detecting abnormal situations, but they cannot determine the sensor variable responsible for the abnormal condition. The objective of the isolation procedure consists of identifying the root cause or variable responsible for the abnormal condition. Here, an isolation procedure based on contribution plots is used to determine the variable responsible for improper sitting posture. The residuals from the model described in equation (10) are plotted as a bar graph to get the contribution plot. The relative size of the bar graph indicates the contribution of each variable to residuals. A high value on the bar graph indicates the variable that is responsible for the improper sitting posture. Kantorovich Distance Kantorovich Distance belongs to the family of optimal mass transport theory primarily used to locate data from one distribution to the other. For any two distributions, A and B, the Kantorovich Distance can be defined as the mode of transferring a mass of data from the first distribution to the second relative to a cost function. The Kantorovich Distance between the two distributions can be computed in the following manner [12]: 𝑊2(𝐴, 𝐵) = (||𝜇𝑎− 𝜇𝑏||2+ 𝑇𝑟(𝛴𝑎+ 𝛴𝑏 − 2(𝛴𝑎12 𝛴𝑏 𝛴𝑎12)12)) 1 2 (11) In the above expression, 𝜇𝑎 and 𝜇𝑏correspond to the means while 𝛴𝑎 and 𝛴𝑏represent the
covariance matrix of the two distributions. The distance between distributions A and B will be small if they are similar and large if they are dissimilar. This concept can be taken forward to be utilized for abnormal event detection problems that involve comparing normal normal data with abnormal data. For continuous time-series data, the KD metric can be computed using a segmentation process where data from both distributions are stacked into different segments followed by segment-to-segment comparison to ensure that the minor details in both distributions are captured very efficiently. This property has made the KD-based statistic very effective to be applied in the domain of abnormal event detection problems.
ICA-KD monitoring approach
This paper integrates the ICA model with the KD-based detector to identify incorrect sitting postures of wheelchair users. Initially, the normal sitting posture wheelchair data X is
normalized to zero mean, and then a reference ICA model is developed. The developed ICA model is used to generate the residuals R1 using the following expression:
𝐑𝟏 = 𝐗 − 𝐗̂ (12) where 𝐗̂ = 𝐐−𝟏𝐕𝐤𝐖𝐤𝐗. Using the model parameters, the reference threshold is computed for the abnormal event detectors using the KDE technique. Next, the abnormal sitting posture wheel-chair data Y is obtained and normalized to have zero mean. Using the reference ICA model parameters, the residuals R2 are generated for the abnormal data Y as follows:
𝐑𝟐 = 𝐘 − 𝐘̂ (13) where 𝐘̂ = 𝐐−𝟏𝐕𝐤𝐖𝐤𝐘. Next, the KD statistic is computed between the residuals R1 and R2 using equation (11) and compared with the reference threshold. Any improper sitting posture will be highlighted whenever the value of the KD statistic exceeds the threshold.
Results and Discussion
Data Description
This part is dedicated to evaluating the efficiency of the proposed approach in detecting abnormal sitting poses in a wheelchair. The experiments are accomplished through actual data from a publicly available database provided in [5]. Two types of sensors are used to collect pressure: an HC-SR04 ultrasonic ranging sensor (https://www.sparkfun.com/products/15569) and pressure sensor (https://www.sparkfun.com/products/9375). Three pressure sensors are placed on the seating (S1, S2, S3), and an ultrasound sensor is placed on the backrest (S4) of the wheelchair (Figure 2). The ultrasonic sensor is employed to measure the distance separating the backrest and the wheelchair back.
Fig.2. Wheelchair with pressure and ultrasonic sensors.
The dataset is relatively small, with 308 data points from the four sensors. Specifically, there are 88 data points collected under the right sitting posture, 100 data points under higher pressure on the right side, 20 data points under higher pressure on the left side, and 100 data points under higher forward pressure. For more details on this data, see [5]. Figure 3 displays the distribution of the collected pressure and distance variables, which indicates that these datasets are non- Gaussian distributed. It would challenge traditional dimensionality reduction techniques, such as PCA, designed based on the Gaussian assumption of input data. Thus, the ICA model designed for non-Gaussian data dimension reduction could be promising.
Fig.3. Distribution of the considered pressure and distance data.
The introduced monitoring strategy aims to alert an improper posture to prevent major health problems. Four scenarios with different sitting postures, which have a distinct impact on the backbone, are considered to study the detection performance of the ICA-KD approach (Fig 1).
The first scenario considers the wheelchair user setting in the correct position. The second scenario is when the user is sitting with higher pressure on the right side, which could cause possible health problems, such as a muscle imbalance, stress on the liver, and respiratory issues.
The third scenario considers the user posture with higher pressure on the left side, which may result in muscle imbalance, stress on the spleen, and respiratory issues. The last scenario is conducted when the user is sitting with higher forward pressure, which may cause health problems, like back pain and knee issues. The aim is to alert the user to change sitting posture when detecting a bad posture persistently staying at least two minutes.
Detection Results
In this section, three experiments are conducted to demonstrate the benefit of the proposed ICA- KD scheme in detecting different improper sitting postures on the wheelchair. Six multivariate monitoring charts, including PCA-based 𝑇2, squared prediction error (SPE), and KD monitoring charts, and three conventional ICA charts, were considered the reference methods for comparison. The anomaly detection rate (ADR), false alarm rate (FAR), and F1-score statistical scores are employed to have a fair comparison between different methods [13]. The ADR is defined as the ratio of abnormal samples that exceed the reference threshold to the number of abnormal samples while FAR is defined as the ratio of number of normal samples that exceed the threshold to the number of normal samples in the data. At first, the models were trained based on training data (i.e., the first 70 anomaly-free data points). The remaining are considered testing data for the three abnormal cases corresponding to different incorrect sitting postures. The PCA and ICA models are developed using the training data with three optimum PCs and ICs chosen using the percentage of total variance (CPV) technique.
Scenario 1: Sitting Posture with higher pressure on the right side
Now, the aim is to check the feasibility of the proposed ICA-KD scheme in identifying improper sitting posture due to high pressure being applied on the right side of the human body. The detection scores (i.e., ADR, FAR and F1-score) in identifying this posture are listed in Table 1.
We observe that both PCA and ICA-based schemes can detect this wrong posture with a good detection rate. In this scenario, the proposed ICA-KD approach may arguably be the best detector by correctly detecting this abnormal posture sitting without false alarm. The ICA-𝐼𝑑2 scheme followed it by achieving a high ADR of 99 and FAR of 1. Results for the first scenario demonstrate the superiority of ICA-KD compared to the other methods.
Scenario 2: Sitting posture with higher pressure on the left side
Here, the aim is to evaluate the capacity of the considered schemes to sense improper sitting posture with high pressure on the left side of the human body. The detection results of the seven procedures are reported in Table 1. In this scenario, the PCA-SPE, PCA-KD, ICA - 𝐼𝑑2, ICA - 𝐼𝑒2, and ICA - SPE-based monitoring schemes detect this improper sitting posture but with several missing detections by reaching an ADR of 66.67, 82.35, 82.35, 94.74, 91.89, respectively.
However, the ICA-KD-based scheme provides better performance since it precisely identifies this improper sitting posture with a good detection rate. This scenario confirms the superior performance of the ICA-KD scheme compared to the other methods.
Table 1: Detection results of the investigated schemes for the three scenarios.
Scenario 1 Scenario 2 Scenario 3
Methods ADR FAR F1-score ADR FAR F1-score ADR FAR F1-score PCA - 𝑇2 99.00 1.43 99.50 95.00 1.43 95.00 74.00 1.43 84.57 PCA - SPE 98.00 5.71 97.50 60.00 5.71 66.67 8.25 7.14 15.86 PCA - KD 100.00 0.00 98.90 70.00 0.00 82.35 24.24 0.00 38.71 ICA - 𝐼𝑑2 99.00 1.00 99.50 70.00 0.00 82.35 80.00 0.00 88.80 ICA - 𝐼𝑒2 84.00 7.00 87.96 90.00 0.00 94.74 71.12 3.00 80.61 ICA - SPE 78.00 1.12 88.15 85.00 0.00 91.89 91.00 0.00 95.29 ICA - KD 100.00 0.00 100.00 100.00 0.00 100.00 98.25 0.00 98.98
Scenario 3: Sitting posture with forward pressure
Finally, we consider the scenario when the improper sitting posture is due to applying high forward pressure. The PCA and ICA-based detection schemes results are summarized in Table 1. The ICA-KD approach achieved better detection results with a high detection rate of 98.25%.
Visually, we can also see that ICA-KD reaches superior performance compared to the other models (Figure 4). The PCA-based conventional indicators demonstrate poor detection performance as they fail to detect this abnormal posture. Here, the red shaded zone highlights the period of the abnormal posture. We omit the visualization of the results of the other sitting postures because they all provide relatively similar conclusions.
Fig.4. Detection results under position 3 sitting posture: (a) PCA - 𝑇2, (b) PCA-SPE, (c) PCA-KD, (d) ICA-𝐼𝑑2, (e) ICA-𝐼𝑒2, (f)ICA - SPE, and (g) ICA-KD.
The ICA-KD approach can detect the presence of abnormal sitting positions but does not identify the types of the detected sitting position. Identifying the type of sitting position is accomplished by analyzing the contribution plots of each variable. Figure 5 demonstrates contribution plots for improper sitting postures in the wheelchair. From Figure 5 (a), we observe a larger pressure level in S1, which is placed under the right leg, than the pressure levels in S2 and S3, which indicates the presence of a wrong sitting posture inclined on the right side.
Similarly, for position sitting 3, we observe a larger pressure recorded by the sensor S3, which is placed under the left leg, compared to the pressure levels recorded by the other sensors (Figure 5 (b)). In a similar way, from Figure 5(c), we observe higher forward pressure in S1, which is placed under the coccyx, than pressure levels recorded by S1 and S3. Importantly, we can see that using the ICA-KD approach and the contribution plot, the proposed strategy can detect and distinguish the type of sitting posture for wheelchair users with unlabeled data.
Conclusion
The number of people using wheelchairs has increased in the last few years, and hence, identifying improper sitting postures in wheelchairs is necessary to avoid health issues. This paper proposed an unsupervised monitoring approach that integrated the ICA model with a KD- based abnormal event indicator to monitor improper sitting postures in the wheelchair. The proposed ICA-KD method used the normal event data from pressure sensors embedded in the wheelchair to develop an ICA model, which was then used for identifying the incorrect sitting postures using KD statistics. The proposed approach was used to detect wrong sitting postures on wheelchairs using information from pressure sensors which showed large pressure levels when more pressure was applied in the right side, left side, and forward direction. The ICA-KD approach over-performed the conventionally applied abnormal event indicators of ICA and PCA-based methods in detecting improper sitting postures. The ICA-KD strategy reached high detection with an F1-score of 99.41%, better than the conventional anomaly indicators. Hence, integrating the ICA model and KD-based detector is a promising tool for detecting abnormal sitting postures in the wheelchair. Furthermore, a contribution plot is conducted to discriminate
between distinct sitting postures. In short, the proposed ICA-KD approach can detect and distinguish sitting postures for wheelchair users.
Fig.5. Variables contribution plot: (a) 2nd position at sample point 100, (b) 3rd position at sample point 200, and (c) fourth position at sample point 250.
Despite the enhanced detection performance achieved by the proposed approach, future works will improve its ability to automatically discriminate different abnormal sitting types. One possible way to alleviate this limitation is to incorporate a classification stage based on a support vector machine or other classifier applied to detected sequences. Another alternative is to apply univariate monitoring charts, such as the generalized likelihood ratio test [14], to the residuals from the ICA model. Furthermore, another direction of improvement consists of using data augmentation techniques to generate large-sized data, which improves the construction of models and thus enhances the detection process.
Furthermore, it is interesting to incorporate more information from posture variations, such as the use of armrests, and foot position, to further improve abnormal postures recognition. Also, other types of sitting postures could be considered.
Nowadays, numerous small sensors are available and can be embedded in daily gadgets, like smartphones and smartwatches, which enable the collection of different variables to monitor the state of wheelchair users. Future work will investigate including more data inputs, such as heart rate and blood pressure provided by a smartwatch or a smartphone, for further improving the efficiency of sitting posture recognition.
Acknowledgements
This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019- CRG7-3800. The authors would like to thank the Manipal Institute of Technology (MIT), Manipal Academy of Higher Education (MAHE), Manipal, for supporting this work.
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K.Ramakrishna Kini ([email protected]) received M.Tech. Degree in Control Systems and Ph.D. degree in advanced control system from Manipal Institute of Technology Manipal, Karnataka India. He is currently an Assistant Professor with Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, India.
His research interests are in data-driven fault detection and diagnosis, soft sensors, and machine learning methods.
Fouzi Harrou ([email protected]) received an M.Sc. degree in telecommunications and networking from the University of Paris VI in 2006 and a Ph.D. degree in systems optimization and security in 2010 from the University Technology of Troyes (UTT), France.
Dr. Harrou’s research interests are in statistical anomaly detection and process monitoring with a particular emphasis on data-driven, machine learning/deep learning methods. He is IEEE senior member.
Muddu Madakyaru ([email protected]) received the M.Tech. degree from the NITK, India, a Ph.D. degree in process control from the IIT Bombay, Mumbai, India and Post-Doctoral Researcher with Texas A&M University, Doha, Qatar. He is currently a Professor with the Department of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of
Higher Education, India. His research interests are in advanced process control, including system identification, fault detection and diagnosis, and machine learning methods.
Farid Kadri ([email protected]) received an M.Sc. degree in systems optimization and security from the University of Technology of Troyes, France, in 2009, and a Ph.D. degree in automation and computer engineering from the University of Valenciennes and Hainaut- Cambresis, France, in 2014. His current research interests include statistical decision theory and its applications, machine learning, predictive modeling, fault detection and monitoring, big data, and decision support systems.
Ying Sun ([email protected]) is an Associate Professor of Statistics in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE). She joined King Abdullah University of Science and Technology (KAUST) in June 2014 after one-year service as an Assistant Professor in the Department of Statistics at the Ohio State University, USA. At KAUST, she leads a multidisciplinary research group on environmental statistics, dedicated to developing statistical models and methods for space-time data to solve important environmental problems.
