Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion

Bourahmoune, Katia; Ishac, Karlos; Amagasa, Toshiyuki

doi:10.3390/s22145337

Cited by 32 publications

(19 citation statements)

References 41 publications

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“…Therefore, the use of only raw (or calibrated) wearable sensor data such as the acceleration and angular velocity for the classification of activities is a growing field of research, with promising results. Especially in the able-bodied population, IMUs or embedded sensors (e.g., smartphones, smartwatches) have been shown to be a valuable tool to monitor activities in a free-living environment [ 40 , 41 , 42 ], but only a few studies have investigated activity detection and classification among MWUs [ 37 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ]. Previous research examining the use of wearable sensors has predominantly focused on physical activity detection to estimate the activity levels and energy expenditure in MWUs with SCI [ 50 , 51 , 52 , 53 , 54 , 55 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Wheelchair Related Shoulder Loading Activities from Wearable Sensor Data: A Machine Learning Approach

Vries

Amrein

Arnet

et al. 2022

Sensors

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Shoulder problems (pain and pathology) are highly prevalent in manual wheelchair users with spinal cord injury. These problems lead to limitations in activities of daily life (ADL), labor- and leisure participation, and increase the health care costs. Shoulder problems are often associated with the long-term reliance on the upper limbs, and the accompanying “shoulder load”. To make an estimation of daily shoulder load, it is crucial to know which ADL are performed and how these are executed in the free-living environment (in terms of magnitude, frequency, and duration). The aim of this study was to develop and validate methodology for the classification of wheelchair related shoulder loading ADL (SL-ADL) from wearable sensor data. Ten able bodied participants equipped with five Shimmer sensors on a wheelchair and upper extremity performed eight relevant SL-ADL. Deep learning networks using bidirectional long short-term memory networks were trained on sensor data (acceleration, gyroscope signals and EMG), using video annotated activities as the target. Overall, the trained algorithm performed well, with an accuracy of 98% and specificity of 99%. When reducing the input for training the network to data from only one sensor, the overall performance decreased to around 80% for all performance measures. The use of only forearm sensor data led to a better performance than the use of the upper arm sensor data. It can be concluded that a generalizable algorithm could be trained by a deep learning network to classify wheelchair related SL-ADL from the wearable sensor data.

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Section: Introductionmentioning

confidence: 99%

Classification of Wheelchair Related Shoulder Loading Activities from Wearable Sensor Data: A Machine Learning Approach

Vries

Amrein

Arnet

et al. 2022

Sensors

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“…They are generally small, accurate, and robust to external conditions. Due to these features, they are still used for some specific applications and are preferred to cameras [7,8]. However, they also have some drawbacks, such as being more expensive, more cumbersome, more prone to noise and drift, and less scalable to multiple individuals.…”

Section: Vision-based Approaches For Posture Classificationmentioning

confidence: 99%

Exploring the Use of Contrastive Language-Image Pre-Training for Human Posture Classification: Insights from Yoga Pose Analysis

Dobrzycki,

Bernardos,

Bergesio

et al. 2023

Mathematics

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Accurate human posture classification in images and videos is crucial for automated applications across various fields, including work safety, physical rehabilitation, sports training, or daily assisted living. Recently, multimodal learning methods, such as Contrastive Language-Image Pretraining (CLIP), have advanced significantly in jointly understanding images and text. This study aims to assess the effectiveness of CLIP in classifying human postures, focusing on its application in yoga. Despite the initial limitations of the zero-shot approach, applying transfer learning on 15,301 images (real and synthetic) with 82 classes has shown promising results. The article describes the full procedure for fine-tuning, including the choice for image description syntax, models and hyperparameters adjustment. The fine-tuned CLIP model, tested on 3826 images, achieves an accuracy of over 85%, surpassing the current state-of-the-art of previous works on the same dataset by approximately 6%, its training time being 3.5 times lower than what is needed to fine-tune a YOLOv8-based model. For more application-oriented scenarios, with smaller datasets of six postures each, containing 1301 and 401 training images, the fine-tuned models attain an accuracy of 98.8% and 99.1%, respectively. Furthermore, our experiments indicate that training with as few as 20 images per pose can yield around 90% accuracy in a six-class dataset. This study demonstrates that this multimodal technique can be effectively used for yoga pose classification, and possibly for human posture classification, in general. Additionally, CLIP inference time (around 7 ms) supports that the model can be integrated into automated systems for posture evaluation, e.g., for developing a real-time personal yoga assistant for performance assessment.

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“…Anwary et al developed a chair seat cover embedded with pressure sensors, which used rule-based classifiers to detect extended periods of sitting in an asymmetrical posture [9]. Bourahmoune et al proposed a machine learning-based sitting posture and stretch recognition system with a pressure-sensing IoT cushion [10]. Towards a low-cost sitting posture monitoring system, some studies explore the use of pressure sensors.…”

Section: Introductionmentioning

confidence: 99%

iGuard: an intelligent sitting posture monitoring system with pressure sensors

Yan,

Wang

2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

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Prolonged and poor sitting postures are major causes of many musculoskeletal disorders, such as herniated discs, cervical spondylosis, back fasciitis, and scoliosis, and hence proper postures are essential for maintaining good health. Towards a pervasive and low-cost healthcare system, we in this study design and implement a sitting posture monitoring system, named iGuard, with a pressure sensor array. Specifically, we use five sensors to sense the sitting posture information and naturally annotate the training sensor readings over a while. Afterward, we segment the streaming data and extract a variety of time-domain and frequency-domain features from the segments to train a sitting posture recognition model. Finally, comparative experiments are conducted to evaluate the performance of iGuard in distinguishing normal, left-leaning, right-leaning, forward-leaning, backward-leaning, left-leg crossed, and right-leg crossed sitting postures. Particularly, we consider three different evaluation schemes, i.e., subject-dependent setting, subject-independent setting, and cross-subject setting. Experimental results demonstrate the effectiveness of iGuard.

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Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion

Cited by 32 publications

References 41 publications

Classification of Wheelchair Related Shoulder Loading Activities from Wearable Sensor Data: A Machine Learning Approach

Classification of Wheelchair Related Shoulder Loading Activities from Wearable Sensor Data: A Machine Learning Approach

Exploring the Use of Contrastive Language-Image Pre-Training for Human Posture Classification: Insights from Yoga Pose Analysis

iGuard: an intelligent sitting posture monitoring system with pressure sensors

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