Portable Ultrasound-Based Device for Detecting Older Adults’ Sit-to-Stand Transitions in Unsupervised 30-Second Chair–Stand Tests

Abstract

Lower-limb strength is a marker of functional decline in elders. This work studies the feasibility of using the quasi-periodic nature of the distance between a subjects’ back and the chair backrest during a 30-s chair–stand test (CST) to carry out unsupervised measurements based on readings from a low-cost ultrasound sensor. The device comprises an ultrasound sensor, an Arduino UNO board, and a Bluetooth module. Sit-to-stand transitions are identified by filtering the signal with a moving minimum filter and comparing the output to an adaptive threshold. An inter-rater reliability (IRR) study was carried out to validate the device ability to count the same number of valid transitions as the gold-standard manual count. A group of elders (age: mean (m) = 80.79 years old, SD = 5.38; gender: 21 female and seven male) were asked to perform a 30-s CST using the device while a trained nurse manually counted valid transitions. Ultimately, a moving minimum filter was necessary to cancel the effect of outliers, likely produced because older people tend to produce more motion artefacts and, thus, noisier signals. While the intra-class correlation coefficient (ICC) for this study was good (ICC = 0.86, 95% confidence interval (CI) = 0.73, 0.93), it is not yet clear whether the results are sufficient to support clinical decision-making.

Unobtrusive Sensors for the Assessment of Older Adult’s Frailty: A Scoping Review

“…These groups of studies show a strong imbalance in favor of studies on activity patterns with the rest of the phenomena having a marginal presence. Even though studies on STS transitions and gait patterns are the most numerous, most of them do not monitor transparent activities: 14 out of 25 STS studies [ 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ] and 17 out of 21 gait studies [ 57 , 58 , 63 , 64 , 70 , 72 , 73 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 ] rely on non-transparent activities.…”

“…The vast majority (all but one) of the studies relying on non-transparent activities involve an instrumented version of a standardized clinical test. The five repetitions chair-stand test (STS5) [ 57 , 59 , 60 , 62 , 63 , 64 , 65 , 66 ] and the 30-s CST [ 58 , 61 , 67 , 68 , 69 , 70 ] in STS studies; the 3 m Walking Test (3-mWT), 4-mWT, 6-minWT, etc. in gait studies [ 57 , 58 , 63 , 64 , 70 , 72 , 73 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 ]; the TUG test in up-and-go studies [ 59 , 62 , 64 , 99 , 100 , 101 , 102 , 103 ]; the SPPB balance test [ 57 , 64 ], quiet standing test [ 63 ], one-foot eyes-closed [ 58 , 70 ] Romberg’s test [ 104 , 105 ], or two-feet eyes-closed [ 86 ] in balance studies; a weight scale in weight studies [ 58 , 70 …”

“…Finally, only five studies involving an IMU on the lower back involved transparent activities [ 50 , 51 , 52 , 72 , 109 ], which represent around a third of all studies with a lower-back-mounted IMU (five out of 14). Some ubiquitous ambient sensors have been used only for non-transparent activities, in particular, distance sensors on furniture/walls [ 57 , 62 , 67 , 77 ], load sensors in furniture [ 57 , 62 , 64 , 77 ], pressure sensors in furniture [ 58 , 70 , 77 , 83 ], and an app in a tablet [ 77 ]. In contrast, transparent activities were present when the ubiquitous ambient sensors did not require the subject to interact with any specific objects.…”

Development of Microcontroller-Based System for Background Radiation Monitoring

“…In Figure 1 , the block diagram of the microcontroller-based radiation monitoring system is shown. The system is built on the Arduino Uno board based on the ATmega328P microcontroller [ 10 , 11 , 12 ].…”

Automatic and Real-Time Computation of the 30-Seconds Chair-Stand Test without Professional Supervision for Community-Dwelling Older Adults

“…Lately, we have explored a different approach ourselves by using an ambient sensor instead of a body-worn sensor [ 33 ]. In our study we explored “… the feasibility of using the quasi-periodic nature of the distance between a subject’s back and the chair backrest during a 30-s CST to carry out unsupervised measurements based on readings from a low-cost ultrasound sensor” [ 33 ], (p. 3). Our sensor comprised an ultrasound sensing module, an Arduino controller board, and a wireless communications module.…”