dresing & pehl GmbH, Marburg) according to predefined transcription rules.

The audio files of all interviews were transcribed with the transcription software f4transkript (by dr. The interviews were audio recorded with the participants’ consent. There are frequent arguments for strengthening the position of older adults as a target group in the research and development of technologies in order to design appropriate solutions. This target group is usually hardly included in development processes or may only be included at a late stage. Older people and their specific needs and usage requirements are not sufficiently considered in recent technology developments. Despite the high prevalence of chronic back pain in older adults, much of the current research in this area mainly addresses the middle-aged segment of the population. In addition, there are also no deep learning solutions whose feedback is specifically intended for non-professionals or patients. Although wireless systems offer some mobility during exercises, no currently available mobile system can be used for deep abdominal and back muscles while analyzing and evaluating muscle location and contraction states via deep learning at the same time. developed a mobile ultrasound system that uses ultrasound imaging to provide feedback on the contraction of the pectoralis major muscle in healthy younger subjects. Meanwhile, in other approaches, mobile wireless ultrasound systems are used to monitor the contraction states of a specific musculature. This makes monitoring of the musculature during dynamic exercise infeasible. However, these approaches only work for high-end stationary ultrasound systems with very good image quality. Such newly developed systems are detecting muscle tissue with almost as much precision as professional human operators. Furthermore, the thickness of muscles in contraction and at rest is being measured automatically within the ultrasound image in real time. Ĭurrently, approaches are being investigated in which muscles are automatically detected in ultrasound imaging via deep learning. The relative thickness of the muscles can be measured using ultrasound. The physiotherapist typically judges a successful isolated contraction of the TrA by observing the increase in the TrA thickness compared to that of the IO and EO. Visual video feedback has proven to be beneficial for learning physical exercise execution and is superior to verbal feedback only. Muscle contraction is often delayed and attenuated in patients with CLB. The lateral abdominal muscles TrA, internal oblique (IO), external oblique (EO), and LM control movement and provide stability to the trunk and spine.

In SSE, the transverse abdominis (TrA) and lumbar multifidus (LM) muscles are particularly important. Most patients must first learn the correct contraction of the muscles. Therapists need to be properly trained to instruct these exercises, and patients with poor body awareness often have difficulty activating deep abdominal and back muscles and maintaining the contraction. However, performing SSE is challenging for both the physiotherapist and patient. The most common form of core stabilization exercises is segmental stabilization exercise (SSE). Core stabilization exercises have a positive effect on pain, proprioception, functional disability, fear of movement, and balance, as well as overall quality of life. The system to be developed was perceived as a helpful solution to support learning about SSE.Ĭore stabilization exercises are a standard procedure in most physiotherapeutic pain treatments, and the benefits for low back pain patients are well documented in the literature. The automated detection and evaluation of muscle contraction states was highlighted as a major benefit of the system compared to the more subjective feedback provided by traditional methods such as palpation. CBPPs reported a high willingness to use the system as a feedback tool both in physiotherapeutic practices and at home. We also gathered information about future-usage scenarios. We interviewed 15 older chronic back pain patients (CBPPs) to investigate their pain management behavior, experience with SSE, as well as their needs and requirements for ULTRAWEAR. ULTRAWEAR is a mobile ultrasound system that provides deep learning-based biofeedback on SSE execution, which is currently under development. Motor learning can be supported using ultrasound imaging as visual biofeedback. The execution of SSE requires the selective contraction of deep abdominal and back muscles. Segmental stabilization exercise (SSE) is often used during physiotherapy to enhance core stability. Chronic back pain has a high prevalence, especially in older adults, and seriously affects sufferers’ quality of life.

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