Body posture recognition and turning recording system for the care of bed bound patients

Hsiao, Rong-Shue; Mi, Zhenqiang; Yang, Bo‐Ru; Kau, Lih‐Jen; Bitew, Mekuanint Agegnehu; Li, Tzu-Yu

doi:10.3233/thc-151088

Cited by 15 publications

(17 citation statements)

References 7 publications

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“…The articles that were included in our analysis were published between 2007 and 2020 and were undertaken in the United States [25][26][27][28][29][30][31][32][33][34][35], China [36][37][38][39][40][41][42][43][44], Spain [45][46][47][48][49][50], Japan [51,52], Italy [53,54], Korea [55], and Greece [56]. According to the applied area of the included studies, we divided the articles into three components: predictive model (12 studies), posture recognition (11 studies), and image analysis (9 studies).…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

Using Machine Learning Technologies in Pressure Injury Management: Systematic Review

Jiang¹,

Ma²,

Guo³

et al. 2021

JMIR Med Inform

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Background Pressure injury (PI) is a common and preventable problem, yet it is a challenge for at least two reasons. First, the nurse shortage is a worldwide phenomenon. Second, the majority of nurses have insufficient PI-related knowledge. Machine learning (ML) technologies can contribute to lessening the burden on medical staff by improving the prognosis and diagnostic accuracy of PI. To the best of our knowledge, there is no existing systematic review that evaluates how the current ML technologies are being used in PI management. Objective The objective of this review was to synthesize and evaluate the literature regarding the use of ML technologies in PI management, and identify their strengths and weaknesses, as well as to identify improvement opportunities for future research and practice. Methods We conducted an extensive search on PubMed, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, China National Knowledge Infrastructure (CNKI), the Wanfang database, the VIP database, and the China Biomedical Literature Database (CBM) to identify relevant articles. Searches were performed in June 2020. Two independent investigators conducted study selection, data extraction, and quality appraisal. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results A total of 32 articles met the inclusion criteria. Twelve of those articles (38%) reported using ML technologies to develop predictive models to identify risk factors, 11 (34%) reported using them in posture detection and recognition, and 9 (28%) reported using them in image analysis for tissue classification and measurement of PI wounds. These articles presented various algorithms and measured outcomes. The overall risk of bias was judged as high. Conclusions There is an array of emerging ML technologies being used in PI management, and their results in the laboratory show great promise. Future research should apply these technologies on a large scale with clinical data to further verify and improve their effectiveness, as well as to improve the methodological quality.

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Section: Characteristics Of Included Studiesmentioning

confidence: 99%

Using Machine Learning Technologies in Pressure Injury Management: Systematic Review

Jiang¹,

Ma²,

Guo³

et al. 2021

JMIR Med Inform

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“…In the case of falls, it is the sensory motion detection that is predominantly conclusive. However, nursing scientists have repeatedly reported that falls in the elderly can have causes attributed to multiple intrinsic and extrinsic factors [36,48,49]. A simple motion measurement cannot account for all this, of which professional caregivers are surely aware of.…”

Section: Underestimated Nursing Problemsmentioning

confidence: 99%

“…Other SeTe devices could be used in secret. For example, motionsensing mats [32] or carpets [33], SeTe devices under [32,34] or on an edge [35] of a mattress, under a bed sheet [36], or as a contact sensor [17] can easily be installed in secret. The question of whether SeTe surveillance could potentially be secretly installed was not discussed in any study.…”

Section: Technical Problems and Ethical Considerationsmentioning

confidence: 99%

A critical systematic review and synopsis of the alignment of scientific developments in surveillance technology in nursing care facilities

Fehling¹,

Dassen²

2017

J Nurs

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Background: The deficits in the self-care of older people are often associated with a loss of autonomy and if care is not provided in the home environment by relatives or professional caregivers, then the self-care deficits may result in the admission to a residential care facility (RCF). On the other hand, in industrialized countries, the recruitment and long-term retention of professional caregivers are becoming increasingly difficult. Technical aids such as sensor technology (SeTe) could solve this dilemma and improve the autonomy, the quality of life, and the safety of the residents. Objectives: To analyze the nursing problems targeted in the studies, where was the SeTe used, and why has it been chosen, the technical limitations of SeTe and whether the studies contain ethical considerations. Design: This review followed the Cochrane Collaboration methods for systematic reviewing. The databases EBSCOhost (which includes CINAHL, MEDLINE, and PsycINFO), Web of Science, Cochrane Library, and Google Scholar were searched. Methods: This critical systematic review analyzes publications on SeTe if the nursing problems referred to by scientists have been in connection with residents of RCFs. Studies that focused on participants in hospitals and home environments or those using gaming consoles or medical applications were excluded. Results: Of the 49 studies included, 31% used only accelerometers, 20% used cameras, and 29% combined various types of SeTe devices. The use of various SeTe was beneficial, possibly because the more perspective data are combined. 51% preferred non-wearable SeTe devices, ⅔ preferred the permanent surveillance by SeTe devices. The nursing problems which the research teams concentrated on were fall risk, immobility, agitation and sleep problems. Only ¼ of the studies mention current or potential ethical considerations regarding the surveillance of residents by SeTe devices. Conclusion: It is gratifying that the international interest in researching SeTe in RCFs has increased. Ethical dilemmas were hardly discussed. The future use of SeTe in RCFs is certainly desirable and unavoidable, but consulting the residents, professional caregivers, and nursing scientists about their requirements for useful and necessary technical surveillance first seems usefull. Relevance to Clinical Practice: Currently RCFs should continue to use the SeTe cautiously. Professional caregivers in RCFs should be able to convey the growing self-image of nursing into similar SeTe research projects.

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“…Nursing homes and hospitals use set programs to avoid the development of bed-sores in bedridden patients, such as pressure-shifting on a regular basis and using cushions with pressure-relief components. The sleeping posture of bed-bound patients needs to be regularly changed in order to reduce the risk of developing pressure ulcers [ 1 ], and bed sensor systems have been developed to recognize sleeping postures.…”

Section: Introductionmentioning

confidence: 99%

“…For bed-bound and limited-mobility patients, the sensor system employed is usually simple and inexpensive, but may achieve a high posture recognition accuracy when the patient is placed by the caregiver in the central axis of the bed. However, the accuracy drops acutely with deviation of the patient’s body from the central axis [ 1 , 10 ]. For ambulatory patients or healthy elderly people, the design becomes much more complicated and relatively more expensive, as human beings exhibit a variety of postures during sleep.…”

Section: Introductionmentioning

confidence: 99%

Sleeping posture recognition using fuzzy c-means algorithm

et al. 2018

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BackgroundPressure sensors have been used for sleeping posture detection, which meet privacy requirements. Most of the existing techniques for sleeping posture recognition used force-sensitive resistor (FSR) sensors. However, lower limbs cannot be recognized accurately unless thousands of sensors are deployed on the bedsheet.MethodWe designed a sleeping posture recognition scheme in which FSR sensors were deployed on the upper part of the bedsheet to record the pressure distribution of the upper body. In addition, an infrared array sensor was deployed to collect data for the lower body. Posture recognition was performed using a fuzzy c-means clustering algorithm. Six types of sleeping body posture were recognized from the combination of the upper and lower body postures.ResultsThe experimental results showed that the proposed method achieved an accuracy of above 88%. Moreover, the proposed scheme is cost-efficient and easy to deploy.ConclusionsThe proposed sleeping posture recognition system can be used for pressure ulcer prevention and sleep quality assessment. Compared to wearable sensors and cameras, FSR sensors and infrared array sensors are unobstructed and meet privacy requirements. Moreover, the proposed method provides a cost-effective solution for the recognition of sleeping posture.

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Body posture recognition and turning recording system for the care of bed bound patients

Cited by 15 publications

References 7 publications

Using Machine Learning Technologies in Pressure Injury Management: Systematic Review

Using Machine Learning Technologies in Pressure Injury Management: Systematic Review

A critical systematic review and synopsis of the alignment of scientific developments in surveillance technology in nursing care facilities

Sleeping posture recognition using fuzzy c-means algorithm

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