Sit-To-Stand Movement Evaluated Using an Inertial Measurement Unit Embedded in Smart Glasses-A Validation Study

Examining the validity of smart glasses in measuring spatiotemporal parameters of gait among people with Parkinson's disease

BACKGROUND

Gait impairment is an early marker of Parkinson's disease (PD) and is frequently monitored to evaluate disease progression. Wearable sensors are increasingly being used to quantify gait in the real-world setting among people with PD (pwPD). Particularly, embedding wearables on devices or clothing that are worn daily may represent a useful strategy to improve compliance and regular monitoring of gait.

RESEARCH QUESTION

The current investigation examined the validity of innovative smart glasses to measure gait among pwPD.

METHODS

Participants wore the smart glasses and 6 APDM gait sensors simultaneously, while performing two walking tasks: the 3-meters Timed Up and Go test (TUG) and the 7-meters Stand and Walk (SAW) test. The following spatiotemporal gait parameters were calculated from the data collected using the two different devices: step time, step length, swing percentage, TUG duration, turn duration, and turn velocity.

RESULTS

A total of 31 pwPD (mean age=68.6±8.5 years; 35.48 % female(N=11), mean Unified Parkinson's Disease Rating Scale (UPDRS) total score=32.1±14.7) participated in the study. Smart glasses achieved high validity in measuring step time (ICC=0.92, p=0.01) and TUG duration (ICC=0.96, p=0.03) compared to APDM sensors. On the other hand, the smart glasses did not achieve adequate validity when measuring step length, swing percentage, turn duration or turn velocity.

SIGNIFICANCE

The current study suggests that smart glasses has the potential to measure TUG and step time in individuals living with PD. However, further research is needed to improve algorithms for sensors worn on the head.

A Clustering-Based Approach to Functional and Biomechanical Parameters Recorded with a Pair of Smart Eyeglasses in Older Adults in Order to Determine Physical Performance Groups

Falls and frailty status are often associated with a decline in physical capacity and multifactorial assessment is highly recommended. Based on the functional and biomechanical parameters measured during clinical tests with an accelerometer integrated into smart eyeglasses, the purpose was to characterize a population of older adults through an unsupervised analysis into different physical performance groups. A total of 84 participants (25 men and 59 women) over the age of sixty-five (age: 74.17 ± 5.80 years; height: 165.70 ± 8.22 cm; body mass: 68.93 ± 13.55 kg) performed a 30 s Sit-to-Stand test, a six-minute walking test (6MWT), and a 3 m Timed Up and Go (TUG) test. The acceleration data measured from the eyeglasses were processed to obtain six parameters: the number of Sit-to-Stands, the maximal vertical acceleration values during Sit-to-Stand movements, step duration and length, and the duration of the TUG test. The total walking distance covered during the 6MWT was also retained. After supervised analyses comparison (i.e., ANOVAs), only one of the parameters (i.e., step length) differed between faller groups and no parameters differed between frail and pre-frail participants. In contrast, unsupervised analysis (i.e., clustering algorithm based on K-means) categorized the population into three distinct physical performance groups (i.e., low, intermediate, and high). All the measured parameters discriminated the low- and high-performance groups. Four of the measured parameters differentiated the three groups. In addition, the low-performance group had a higher proportion of frail participants. These results are promising for monitoring activities in older adults to prevent the decline of physical capacities.

Digital technologies for step counting: between promises of reliability and risks of reductionism

Step counting is among the fundamental features of wearable technology, as it grounds several uses of wearables in biomedical research and clinical care, is at the center of emerging public health interventions and recommendations, and is gaining increasing scientific and political importance. This paper provides a perspective of step counting in wearable technology, identifying some limitations to the ways in which wearable technology measures steps and indicating caution in current uses of step counting as a proxy for physical activity. Based on an overview of the current state of the art of technologies and approaches to step counting in digital wearable technologies, we discuss limitations that are methodological as well as epistemic and ethical-limitations to the use of step counting as a basis to build scientific knowledge on physical activity (epistemic limitations) as well as limitations to the accessibility and representativity of these tools (ethical limitations). As such, using step counting as a proxy for physical activity should be considered a form of reductionism. This is not per se problematic, but there is a need for critical appreciation and awareness of the limitations of reductionistic approaches. Perspective research should focus on holistic approaches for better representation of physical activity levels and inclusivity of different user populations.

Validation of the Short Physical Performance Battery via Plantar Pressure Analysis Using Commercial Smart Insoles

This cross-sectional study, conducted at a tertiary care hospital's rehabilitation clinic, aimed to validate Short Physical Performance Battery (SPPB) results obtained through plantar pressure analysis using commercial smart insoles (SPPB-SI) and to compare these results to manually acquired results by an experienced examiner (SPPB-M). This study included 40 independent-walking inpatients and outpatients aged 50 or older. SPPB-SI and SPPB-M were administered concurrently, with the smart insoles providing plantar pressure data that were converted into time-pressure curves. Two interpreters assessed the curves, determining component completion times for the SPPB-SI scores. Among the 40 participants (mean age: 72.98, SD: 9.27), the mean total SPPB-SI score was 7.72 ± 2.50, and the mean total SPPB-M score was 7.95 ± 2.63. The time recordings and measured scores of each SPPB-SI component exhibited high reliability with inter- and intra-interpreter correlation coefficients of 0.9 and 0.8 or higher, respectively. The intraclass correlation coefficient between the total SPPB-SI and SPPB-M scores was 0.831 (p < 0.001), and that between the component scores of the two measurements ranged from 0.837 to 0.901 (p < 0.001). Consistent correlations with geriatric functional parameters were observed for both SPPB-SI and SPPB-M. This study underscores the potential of commercial smart insoles as reliable tools for conducting SPPB assessments.

Reliability of Wearable Sensors for Assessing Gait and Chair Stand Function at Home in People With Knee Osteoarthritis

OBJECTIVE

To assess the reliability of wearable sensors for at-home assessment of walking and chair stand activities in people with knee osteoarthritis (OA).

METHODS

Baseline data from participants with knee OA (n = 20) enrolled in a clinical trial of an exercise intervention were used. Participants completed an in-person laboratory visit and a video conference-enabled at-home visit. In both visits, participants performed walking and chair stand tasks while fitted with 3 inertial sensors. During the at-home visit, participants self-donned the sensors and completed 2 sets of acquisitions separated by a 15-minute break, when they removed and redonned the sensors. Participants completed a survey on their experience with the at-home visit. During the laboratory visit, researchers placed the sensors on the participants. Spatiotemporal metrics of walking gait and chair stand duration were extracted from the sensor data. We used intraclass correlation coefficients (ICCs) and the Bland-Altman plot for statistical analyses.

RESULTS

For test-retest reliability during the at-home visit, all ICCs were good to excellent (0.85-0.95). For agreement between at-home and laboratory visits, ICCs were moderate to good (0.59-0.87). Systematic differences were noted between at-home and laboratory data due to faster task speed during the laboratory visits. Participants reported a favorable experience during the at-home visit.

CONCLUSION

Our method of estimating spatiotemporal gait measures and chair stand duration function remotely was reliable, feasible, and acceptable in people with knee OA. Wearable sensors could be used to remotely assess walking and chair stand in participant's natural environments in future studies.

EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors

Wearable sensors are able to monitor physical health in a home environment and detect changes in gait patterns over time. To ensure long-term user engagement, wearable sensors need to be seamlessly integrated into the user's daily life, such as hearing aids or earbuds. Therefore, we present EarGait, an open-source Python toolbox for gait analysis using inertial sensors integrated into hearing aids. This work contributes a validation for gait event detection algorithms and the estimation of temporal parameters using ear-worn sensors. We perform a comparative analysis of two algorithms based on acceleration data and propose a modified version of one of the algorithms. We conducted a study with healthy young and elderly participants to record walking data using the hearing aid's integrated sensors and an optical motion capture system as a reference. All algorithms were able to detect gait events (initial and terminal contacts), and the improved algorithm performed best, detecting 99.8% of initial contacts and obtaining a mean stride time error of 12 ± 32 ms. The existing algorithms faced challenges in determining the laterality of gait events. To address this limitation, we propose modifications that enhance the determination of the step laterality (ipsi- or contralateral), resulting in a 50% reduction in stride time error. Moreover, the improved version is shown to be robust to different study populations and sampling frequencies but is sensitive to walking speed. This work establishes a solid foundation for a comprehensive gait analysis system integrated into hearing aids that will facilitate continuous and long-term home monitoring.

Applying the UTAUT2 model to smart eyeglasses to detect and prevent falls among older adults and examination of associations with fall-related functional physical capacities: Survey Study (Preprint)

BACKGROUND

As people age, their physical capacities (eg, walking and balance) decline and the risk of falling rises. Yet, classic fall detection devices are poorly accepted by older adults. Because they often wear eyeglasses as they go about their daily activities, daily monitoring to detect and prevent falls with smart eyeglasses might be more easily accepted.

OBJECTIVE

On the basis of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this study evaluated (1) the acceptability of smart eyeglasses for the detection and prevention of falls by older adults and (2) the associations with selected fall-related functional physical capacities.

METHODS

A total of 142 volunteer older adults (mean age 74.9 years, SD 6.5 years) completed the UTAUT2 questionnaire adapted for smart eyeglasses and then performed several physical tests: a unipodal balance test with eyes open and closed, a 10-m walk test, and a 6-minute walk test. An unsupervised analysis classified the participants into physical performance groups. Multivariate ANOVAs were performed to identify differences in acceptability constructs according to the performance group.

RESULTS

The UTAUT2 questionnaire adapted for eyeglasses presented good psychometric properties. Performance expectancy (β=.21, P=.005), social influence (β=.18, P=.007), facilitating conditions (β=.17, P=.04), and habit (β=.40, P<.001) were significant contributors to the behavioral intention to use smart eyeglasses (R²=0.73). The unsupervised analysis based on fall-related functional physical capacities created 3 groups of physical performance: low, intermediate, and high. Effort expectancy in the low performance group (mean 3.99, SD 1.46) was lower than that in the other 2 groups (ie, intermediate: mean 4.68, SD 1.23; high: mean 5.09, SD 1.41). Facilitating conditions in the high performance group (mean 5.39, SD 1.39) were higher than those in the other 2 groups (ie, low: mean 4.31, SD 1.68; intermediate: mean 4.66, SD 1.51).

CONCLUSIONS

To our knowledge, this study is the first to examine the acceptability of smart eyeglasses in the context of fall detection and prevention in older adults and to associate acceptability with fall-related functional physical capacities. The older adults with higher physical performances, and possibly lower risks of falling, reported greater acceptability of smart eyeglasses for fall prevention and detection than their counterparts exhibiting low physical performances.

Smart Eyeglasses: A Valid and Reliable Device to Assess Spatiotemporal Parameters during Gait

The study aims to determine the validity and reproducibility of step duration and step length parameters measured during walking in healthy participants using an accelerometer embedded in smart eyeglasses. Twenty young volunteers participated in two identical sessions comprising a 30 s gait assessment performed at three different treadmill speeds under two conditions (i.e., with and without a cervical collar). Spatiotemporal parameters (i.e., step duration and step length normalized by the lower limb length) were obtained with both the accelerometer embedded in smart eyeglasses and an optoelectronic system. The relative intra- and inter-session reliability of step duration and step length computed from the vertical acceleration data were excellent for all experimental conditions. An excellent absolute reliability was observed for the eyeglasses for all conditions and concurrent validity between systems was observed. An accelerometer incorporated in smart eyeglasses is accurate to measure step duration and step length during gait.

The Validity of Wireless Earbud-Type Wearable Sensors for Head Angle Estimation and the Relationships of Head with Trunk, Pelvis, Hip, and Knee during Workouts

The present study was performed to investigate the validity of a wireless earbud-type inertial measurement unit (Ear-IMU) sensor used to estimate head angle during four workouts. In addition, relationships between head angle obtained from the Ear-IMU sensor and the angles of other joints determined with a 3D motion analysis system were investigated. The study population consisted of 20 active volunteers. The Ear-IMU sensor measured the head angle, while a 3D motion analysis system simultaneously measured the angles of the head, trunk, pelvis, hips, and knees during workouts. Comparison with the head angle measured using the 3D motion analysis system indicated that the validity of the Ear-IMU sensor was very strong or moderate in the sagittal and frontal planes. In addition, the trunk angle in the frontal plane showed a fair correlation with the head angle determined with the Ear-IMU sensor during a single-leg squat, reverse lunge, and standing hip abduction; the correlation was poor in the sagittal plane. Our results indicated that the Ear-IMU sensor can be used to directly estimate head motion and indirectly estimate trunk motion.

Validity of a simple sit-to-stand method for assessing force-velocity profile in older adults

BACKGROUND

Lower limb muscle strength is an important determinant of physical function in older adults. However, its measure in clinical settings is limited because of the requirement for large-scale and costly equipment. A new simple protocol based on sit-to-stand test (STS) is developed to measure force velocity (F-v) and power velocity (P-v) profile in the community-dwelling older adults.

OBJECTIVE

The objective of this study was to assess the validity of this new methodology for measuring F-v and P-v profile compared to the gold standard isokinetic BIODEX.

PARTICIPANTS

46 older people aged 65-85 years (M = 73.7; SD = 7.7).

METHODS

F-v and P-v profiles were assessed in participants on their dominant leg. The concurrent validity of STS was tested using Spearman's rank correlation coefficient and Passing Bablok: maximal power output Pmax, optimal velocity and force Vopt and Fopt, maximal force at null velocity F0, maximal unloaded velocity V0 and coefficient of F-v (S_FV) and P-v equation (a_poly, b_poly).

RESULTS

No proportional difference for F0 and b_poly and a low significant correlation for Pmax (r = 0.314), S_fv (r = 0.229), a_poly (r = 0.335) and b_poly (r = 0.226) whereas the other parameters were non correlated significantly.

CONCLUSION

STS method is moderately reliable on force and power parameters whereas further improvements are needing for velocity parameters. However, its feasibility, portability and lower cost compared to other methods makes it very affordable in clinical context and will allow easy investigation of aging population.

Comparison of Human Interpretation and a Rule-Based Algorithm for Instrumented Sit-to-Stand Test

BACKGROUND

The five times sit-to-stand test (5STS) is one of the most commonly used tests to assess the physical performance of lower extremities. This study assessed the correlation between human interpretation (5STShuman) and a rule-based algorithm (5STSrule) using instrumented 5STS with two sensors.

METHODS

We analyzed clinical records of 148 patients who visited the geriatric outpatient clinic of Asan Medical Center between December 2020 and March 2021 and underwent physical performance assessment using the electronic Short Physical Performance Battery (eSPPB) protocol. For STS, time-weight and time-distance curves were constructed using a loadcell and light detection and ranging (LiDAR). We manually assessed the grids of these curves to calculate 5STShuman, while 5STSrule used an empirical rule-based algorithm.

RESULTS

In the study population, the mean 5STShuman and 5STSrule times, i.e., 12.2±0.4 and 11.4±0.4 seconds, respectively, did not differ significantly (p=0.232). Linear regression analysis showed that 5STShuman and 5STSrule were positively correlated (β=0.99, R2=0.99). The measures also did not differ (p=0.381) in classifying sarcopenia according to the Asian Working Group Society criteria, with C-indices of 0.826 for 5STShuman and 0.820 for 5STSrule.

CONCLUSION

An empirical rule-based algorithm correlated with human-interpreted 5STS and had comparable classification ability for sarcopenia.