Influence of motion therapy on daily life activities of people with lumbar pain syndrome

Kaljić, Eldad; Pašalić, Arzija; Katana, Bakir; Hadžiomerović, Amra Mačak; Bojičić, Samir; Jaganjac, Amila; Salkić, Naim

doi:10.17532/jhsci.2022.1975

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…This technology could also foster a heightened sense of body awareness, facilitating the adoption of healthier movement patterns and postures [50,66]. Such heightened awareness may prove particularly beneficial for individuals grappling with postural abnormalities or lumbo-pelvic injuries, enabling them to actively incorporate compensatory behaviors into their daily activities [26,60,63,78]. Furthermore, wearable devices measuring spinal posture have potential applications in the prevention, monitoring, and treatment of LBD.…”

Section: Practical Applicationsmentioning

confidence: 99%

Lumbo-Pelvic Rhythm Monitoring Using Wearable Technology with Sensory Biofeedback: A Systematic Review

García-Jaén,

Sebastia-Amat,

Sanchis-Soler

et al. 2024

Healthcare

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As an essential lower-back movement pattern, lumbo-pelvic rhythm (LPR) during forward trunk flexion and backward return has been investigated on a large scale. It has been suggested that abnormalities in lumbo-pelvic coordination are related to the risk of developing low back disorders. However, considerable differences in the approaches used to monitor LPR make it challenging to integrate findings from those investigations for future research. Therefore, the aim of this systematic review was to summarize the use of wearable technology for kinematic measurement with sensory biofeedback for LPR monitoring by assessing these technologies’ specific capabilities and biofeedback capacities and exploring their practical viability based on sensor outcomes. The review was developed following the PRISMA guidelines, and the risk of bias was analyzed using the PREDro and STROBE scales. PubMed, Web of Science, Scopus, and IEEEXPLORE databases were searched for relevant studies, initially returning a total of 528 articles. Finally, we included eight articles featuring wearable devices with audio or vibration biofeedback. Differences in protocols and limitations were also observed. This novel study presents a review of wearable tracking devices for LPR motion-mediated biofeedback for the purpose of correcting lower back posture. More research is needed to determine the long-term effectiveness of these devices, as well as their most appropriate corresponding methodologies.

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Section: Practical Applicationsmentioning

confidence: 99%

Lumbo-Pelvic Rhythm Monitoring Using Wearable Technology with Sensory Biofeedback: A Systematic Review

García-Jaén,

Sebastia-Amat,

Sanchis-Soler

et al. 2024

Healthcare

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Recognition of Daily Activities in Adults With Wearable Inertial Sensors: Deep Learning Methods Study

De Ramón Fernández,

Ruiz Fernández,

García Jaén

et al. 2024

JMIR Med Inform

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Background Activities of daily living (ADL) are essential for independence and personal well-being, reflecting an individual’s functional status. Impairment in executing these tasks can limit autonomy and negatively affect quality of life. The assessment of physical function during ADL is crucial for the prevention and rehabilitation of movement limitations. Still, its traditional evaluation based on subjective observation has limitations in precision and objectivity. Objective The primary objective of this study is to use innovative technology, specifically wearable inertial sensors combined with artificial intelligence techniques, to objectively and accurately evaluate human performance in ADL. It is proposed to overcome the limitations of traditional methods by implementing systems that allow dynamic and noninvasive monitoring of movements during daily activities. The approach seeks to provide an effective tool for the early detection of dysfunctions and the personalization of treatment and rehabilitation plans, thus promoting an improvement in the quality of life of individuals. Methods To monitor movements, wearable inertial sensors were developed, which include accelerometers and triaxial gyroscopes. The developed sensors were used to create a proprietary database with 6 movements related to the shoulder and 3 related to the back. We registered 53,165 activity records in the database (consisting of accelerometer and gyroscope measurements), which were reduced to 52,600 after processing to remove null or abnormal values. Finally, 4 deep learning (DL) models were created by combining various processing layers to explore different approaches in ADL recognition. Results The results revealed high performance of the 4 proposed models, with levels of accuracy, precision, recall, and F1-score ranging between 95% and 97% for all classes and an average loss of 0.10. These results indicate the great capacity of the models to accurately identify a variety of activities, with a good balance between precision and recall. Both the convolutional and bidirectional approaches achieved slightly superior results, although the bidirectional model reached convergence in a smaller number of epochs. Conclusions The DL models implemented have demonstrated solid performance, indicating an effective ability to identify and classify various daily activities related to the shoulder and lumbar region. These results were achieved with minimal sensorization—being noninvasive and practically imperceptible to the user—which does not affect their daily routine and promotes acceptance and adherence to continuous monitoring, thus improving the reliability of the data collected. This research has the potential to have a significant impact on the clinical evaluation and rehabilitation of patients with movement limitations, by providing an objective and advanced tool to detect key movement patterns and joint dysfunctions.

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Recognition of Daily Activities in Adults With Wearable Inertial Sensors: Deep Learning Methods Study (Preprint)

De Ramón Fernández,

Ruiz Fernández,

García Jaén

et al. 2024

Preprint

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BACKGROUND Activities of daily living (ADL) are essential for independence and personal well-being, reflecting an individual's functional status. Impairment in executing these tasks can limit autonomy and negatively affect quality of life. The assessment of physical function during ADL is crucial for the prevention and rehabilitation of movement limitations. Still, its traditional evaluation based on subjective observation has limitations in precision and objectivity. OBJECTIVE The primary objective of this study is to use innovative technology, specifically wearable inertial sensors combined with artificial intelligence (AI) techniques, to objectively and accurately evaluate human performance in ADL. It is proposed to overcome the limitations of traditional methods by implementing systems that allow dynamic and non-invasive monitoring of movements during daily activities. The approach seeks to provide an effective tool for the early detection of dysfunctions and the personalization of treatment and rehabilitation plans, thus promoting an improvement in the quality of life of individuals. METHODS To monitor movements (six related to the shoulder and three related to the back), wearable inertial sensors were developed that include accelerometers and triaxial gyroscopes. The initial database consisted of 53,165 activity records, which was reduced to 52,600 after processing to remove null or abnormal values. Finally, four Deep Learning models were created combining various processing layers to explore different approaches in ADL recognition. RESULTS The results revealed high performance of the four proposed models, with levels of accuracy, precision, recall, and F1-score ranging between 95% and 97% for all classes, and an average loss of 0.10. These results indicate the great capacity of the models to accurately identify a variety of activities, with a good balance between precision and recall. Both the convolutional and bidirectional approaches achieved slightly superior results, although the bidirectional model reached convergence in a smaller number of epochs. CONCLUSIONS The Deep Learning models implemented have demonstrated solid performance indicating an effective ability to identify and classify various daily activities related to the shoulder and lumbar region. These results were achieved with minimal sensorization, non-invasive and practically imperceptible to the user, which does not affect their daily routine and promotes acceptance and adherence to continuous monitoring, thus improving the reliability of the data collected. This research has the potential to have a significant impact on the clinical evaluation and rehabilitation of patients with movement limitations, by providing an objective and advanced tool to detect key movement patterns and joint dysfunctions.

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Influence of motion therapy on daily life activities of people with lumbar pain syndrome

Cited by 3 publications

References 36 publications

Lumbo-Pelvic Rhythm Monitoring Using Wearable Technology with Sensory Biofeedback: A Systematic Review

Lumbo-Pelvic Rhythm Monitoring Using Wearable Technology with Sensory Biofeedback: A Systematic Review

Recognition of Daily Activities in Adults With Wearable Inertial Sensors: Deep Learning Methods Study

Recognition of Daily Activities in Adults With Wearable Inertial Sensors: Deep Learning Methods Study (Preprint)

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