Wearable sensors have recently been used to evaluate biomechanical parameters of everyday movements, but few have been located at the head level. This study investigated the relative and absolute reliability (intra- and inter-session) and concurrent validity of an inertial measurement unit (IMU) embedded in smart eyeglasses during sit-to-stand (STS) movements for the measurement of maximal acceleration of the head. Reliability and concurrent validity were investigated in nineteen young and healthy participants by comparing the acceleration values of the glasses’ IMU to an optoelectronic system. Sit-to-stand movements were performed in laboratory conditions using standardized tests. Participants wore the smart glasses and completed two testing sessions with STS movements performed at two speeds (slow and comfortable) under two different conditions (with and without a cervical collar). Both the vertical and anteroposterior acceleration values were collected and analyzed. The use of the cervical collar did not significantly influence the results obtained. The relative reliability intra- and inter-session was good to excellent (i.e., intraclass correlation coefficients were between 0.78 and 0.91) and excellent absolute reliability (i.e., standard error of the measurement lower than 10% of the average test or retest value) was observed for the glasses, especially for the vertical axis. Whatever the testing sessions in all conditions, significant correlations (p < 0.001) were found for the acceleration values recorded either in the vertical axis and in the anteroposterior axis between the glasses and the optoelectronic system. Concurrent validity between the glasses and the optoelectronic system was observed. Our observations indicate that the IMU embedded in smart glasses is accurate to measure vertical acceleration during STS movements. Further studies should investigate the use of these smart glasses to assess the STS movement in unstandardized settings (i.e., clinical and/or home) and to report vertical acceleration values in an elderly population of fallers and non-fallers.

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.

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.

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.

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.

La problématique du risque de chute chez les personnes âgées augmente avec le vieillissement. Les conséquences des chutes étant nombreuses et néfastes, le développement de stratégies de prévention est crucial. Pour estimer le risque de chute, différents tests cliniques fonctionnels sont utilisés mais ces tests sont ponctuels, réalisés en laboratoire et basés sur des seuils de référence qui peuvent varier selon les études. Pour pallier ces inconvénients et permettre un suivi longitudinal des capacités fonctionnelles des personnes âgées, le développement de centrales inertielles semble prometteur. Ainsi, les centrales inertielles sont progressivement intégrées dans des objets de la vie courante afin d'analyser les mouvements de la vie quotidienne. Dans ce travail doctoral, une paire de lunettes équipée avec une centrale inertielle a été développée pour permettre l'analyse des mouvements de la vie quotidienne dans un objectif de détection et de prévention du déclin des capacités physiques. Le premier objectif de ce travail était de valider l'utilité de la paire de lunettes connectées pour évaluer les mouvements de lever de chaise et la marche dans une population de jeunes adultes en bonne santé. Le deuxième objectif de ce projet a consisté à catégoriser une population de personnes âgées dans différents groupes de performance physique grâce à une analyse non-supervisée en se basant sur différents paramètres fonctionnels et biomécaniques obtenus avec les lunettes. Le troisième objectif visait à évaluer l'acceptabilité des lunettes connectées chez les personnes âgées. Enfin, le quatrième objectif était d'évaluer l'efficacité d'un programme d'activité physique adaptée réalisé par visioconférence sur différentes capacités physiques des personnes âgées. Ce travail doctoral a également servi de base au développement d'un algorithme de détection de la chute et de la prévention du déclin des capacités physiques.Les deux premières études ont permis de valider l'utilisation des lunettes connectées, d'un point de vue de la fiabilité, de la reproductibilité des mesures effectuées en comparaison avec les données recueillies par un système de référence (i.e., analyse du mouvement 3D) pour trois paramètres : le pic d'accélération verticale maximale lors des levers de chaise, la durée et la longueur des pas pour la marche. La troisième étude a utilisé l'accéléromètre des lunettes pour évaluer sept paramètres fonctionnels et biomécaniques au cours de trois tests fonctionnels. Trois groupes de performance physique (i.e., faible, intermédiaire et élevée) ont été créés en réalisant une analyse non-supervisée basée sur ces sept paramètres. Cette étude a permis de mettre en évidence qu'une simple définition d'un risque de chute et/ou de classification en fonction d'un phénotype de fragilité n'est pas suffisant pour caractériser une population vieillissante. La quatrième étude qui visait à évaluer l'acceptabilité des lunettes connectées chez des personnes âgées a permis de souligner que l'acceptabilité des lunettes était supérieure à celle des autres technologies utilisées pour la détection de la chute. De plus, de façon originale, le niveau de performance physique des personnes âgées était associé à l'acceptabilité des lunettes. Dans la cinquième étude, la mise en place un programme d'activité physique adaptée de trois mois par visioconférence a permis d'améliorer la puissance des muscles extenseurs du genou et le temps pour réaliser un Timed Up and Go en condition confort. L'ensemble de ces travaux a servi de base pour le développement industriel d'un algorithme de détection de la chute et de prévention du déclin des capacités physiques à partir des données recueillies par les lunettes.