Sensors

Research

18 pages, 2433 KiB

Open AccessArticle

Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics

by Venous Roshdibenam, Gerald J. Jogerst, Nicholas R. Butler and Stephen Baek

Sensors 2021, 21(10), 3481; https://doi.org/10.3390/s21103481 - 17 May 2021

Cited by 25 | Viewed by 5459

Falls among the elderly population cause detrimental physical, mental, financial problems and, in the worst case, death. The increasing number of people entering the higher risk age-range has increased clinicians’ attention to intervene. Clinical tools, e.g., the Timed Up and Go (TUG) test, [...] Read more.

Falls among the elderly population cause detrimental physical, mental, financial problems and, in the worst case, death. The increasing number of people entering the higher risk age-range has increased clinicians’ attention to intervene. Clinical tools, e.g., the Timed Up and Go (TUG) test, have been created for aiding clinicians in fall-risk assessment. Often simple to evaluate, these assessments are subject to a clinician’s judgment. Wearable sensor data with machine learning algorithms were introduced as an alternative to precisely quantify ambulatory kinematics and predict prospective falls. However, they require a long-term evaluation of large samples of subjects’ locomotion and complex feature engineering of sensor kinematics. Therefore, it is critical to build an objective fall-risk detection model that can efficiently measure biometric risk factors with minimal costs. We built and studied a sensor data-driven convolutional neural network model to predict older adults’ fall-risk status with relatively high sensitivity to geriatrician’s expert assessment. The sample in this study is representative of older patients with multiple co-morbidity seen in daily medical practice. Three non-intrusive wearable sensors were used to measure participants’ gait kinematics during the TUG test. This data collection ensured convenient capture of various gait impairment aspects at different body locations. Full article

(This article belongs to the Special Issue Wearable Devices: Applications in Older Adults)

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13 pages, 1710 KiB

Open AccessArticle

The Impact of Weather and Seasons on Falls and Physical Activity among Older Adults with Glaucoma: A Longitudinal Prospective Cohort Study

by Hursuong Vongsachang, Aleksandra Mihailovic, Jian-Yu E, David S. Friedman, Sheila K. West, Laura N. Gitlin and Pradeep Y. Ramulu

Sensors 2021, 21(10), 3415; https://doi.org/10.3390/s21103415 - 14 May 2021

Cited by 13 | Viewed by 2648

Abstract

Understanding periods of the year associated with higher risk for falling and less physical activity may guide fall prevention and activity promotion for older adults. We examined the relationship between weather and seasons on falls and physical activity in a three-year cohort of [...] Read more.

Understanding periods of the year associated with higher risk for falling and less physical activity may guide fall prevention and activity promotion for older adults. We examined the relationship between weather and seasons on falls and physical activity in a three-year cohort of older adults with glaucoma. Participants recorded falls information via monthly calendars and participated in four one-week accelerometer trials (baseline and per study year). Across 240 participants, there were 406 falls recorded over 7569 person-months, of which 163 were injurious (40%). In separate multivariable regression models incorporating generalized estimating equations, temperature, precipitation, and seasons were not significantly associated with the odds of falling, average daily steps, or average daily active minutes. However, every 10 °C increase in average daily temperature was associated with 24% higher odds of a fall being injurious, as opposed to non-injurious (p = 0.04). The odds of an injurious fall occurring outdoors, as opposed to indoors, were greater with higher average temperatures (OR per 10 °C = 1.46, p = 0.03) and with the summer season (OR = 2.69 vs. winter, p = 0.03). Falls and physical activity should be understood as year-round issues for older adults, although the likelihood of injury and the location of fall-related injuries may change with warmer season and temperatures. Full article

(This article belongs to the Special Issue Wearable Devices: Applications in Older Adults)

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15 pages, 2206 KiB

Open AccessArticle

Age Differences in Estimating Physical Activity by Wrist Accelerometry Using Machine Learning

by Mamoun T. Mardini, Chen Bai, Amal A. Wanigatunga, Santiago Saldana, Ramon Casanova and Todd M. Manini

Sensors 2021, 21(10), 3352; https://doi.org/10.3390/s21103352 - 12 May 2021

Cited by 5 | Viewed by 2943

Abstract

Accelerometer-based fitness trackers and smartwatches are proliferating with incessant attention towards health tracking. Despite their growing popularity, accurately measuring hallmark measures of physical activities has yet to be accomplished in adults of all ages. In this work, we evaluated the performance of four [...] Read more.

Accelerometer-based fitness trackers and smartwatches are proliferating with incessant attention towards health tracking. Despite their growing popularity, accurately measuring hallmark measures of physical activities has yet to be accomplished in adults of all ages. In this work, we evaluated the performance of four machine learning models: decision tree, random forest, extreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO), to estimate the hallmark measures of physical activities in young (20–50 years), middle-aged (50–70 years], and older adults (70–89 years]. Our models were built to recognize physical activity types, recognize physical activity intensities, estimate energy expenditure (EE) and recognize individual physical activities using wrist-worn tri-axial accelerometer data (33 activities per participant) from a large sample of participants (n = 253, 62% women, aged 20–89 years old). Results showed that the machine learning models were quite accurate at recognizing physical activity type and intensity and estimating energy expenditure. However, models performed less optimally when recognizing individual physical activities. F1-Scores derived from XGBoost’s models were high for sedentary (0.955–0.973), locomotion (0.942–0.964) and lifestyle (0.913–0.949) activity types with no apparent difference across age groups. Low (0.919–0.947), light (0.813–0.828) and moderate (0.846–0.875) physical activity intensities were also recognized accurately. The root mean square error range for EE was approximately 1 equivalent of resting EE [0.835–1.009 METs]. Generally, random forest and XGBoost models outperformed other models. In conclusion, machine learning models to label physical activity types, activity intensity and energy expenditure are accurate and there are minimal differences in their performance across young, middle-aged and older adults. Full article

(This article belongs to the Special Issue Wearable Devices: Applications in Older Adults)

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14 pages, 957 KiB

Open AccessEditor’s ChoiceArticle

Calibration and Cross-Validation of Accelerometer Cut-Points to Classify Sedentary Time and Physical Activity from Hip and Non-Dominant and Dominant Wrists in Older Adults

by Jairo H. Migueles, Cristina Cadenas-Sanchez, Juan M. A. Alcantara, Javier Leal-Martín, Asier Mañas, Ignacio Ara, Nancy W. Glynn and Eric J. Shiroma

Sensors 2021, 21(10), 3326; https://doi.org/10.3390/s21103326 - 11 May 2021

Cited by 29 | Viewed by 4965

Abstract

Accelerometers’ accuracy for sedentary time (ST) and moderate-to-vigorous physical activity (MVPA) classification depends on accelerometer placement, data processing, activities, and sample characteristics. As intensities differ by age, this study sought to determine intensity cut-points at various wear locations people more than 70 years [...] Read more.

Accelerometers’ accuracy for sedentary time (ST) and moderate-to-vigorous physical activity (MVPA) classification depends on accelerometer placement, data processing, activities, and sample characteristics. As intensities differ by age, this study sought to determine intensity cut-points at various wear locations people more than 70 years old. Data from 59 older adults were used for calibration and from 21 independent participants for cross-validation purposes. Participants wore accelerometers on their hip and wrists while performing activities and having their energy expenditure measured with portable calorimetry. ST and MVPA were defined as ≤1.5 metabolic equivalents (METs) and ≥3 METs (1 MET = 2.8 mL/kg/min), respectively. Receiver operator characteristic (ROC) analyses showed fair-to-good accuracy (area under the curve [AUC] = 0.62–0.89). ST cut-points were 7 mg (cross-validation: sensitivity = 0.88, specificity = 0.80) and 1 count/5 s (cross-validation: sensitivity = 0.91, specificity = 0.96) for the hip; 18 mg (cross-validation: sensitivity = 0.86, specificity = 0.86) and 102 counts/5 s (cross-validation: sensitivity = 0.91, specificity = 0.92) for the non-dominant wrist; and 22 mg and 175 counts/5 s (not cross-validated) for the dominant wrist. MVPA cut-points were 14 mg (cross-validation: sensitivity = 0.70, specificity = 0.99) and 54 count/5 s (cross-validation: sensitivity = 1.00, specificity = 0.96) for the hip; 60 mg (cross-validation: sensitivity = 0.83, specificity = 0.99) and 182 counts/5 s (cross-validation: sensitivity = 1.00, specificity = 0.89) for the non-dominant wrist; and 64 mg and 268 counts/5 s (not cross-validated) for the dominant wrist. These cut-points can classify ST and MVPA in older adults from hip- and wrist-worn accelerometers. Full article

(This article belongs to the Special Issue Wearable Devices: Applications in Older Adults)

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15 pages, 1786 KiB

Open AccessArticle

The Effect of Different Turn Speeds on Whole-Body Coordination in Younger and Older Healthy Adults

by Fuengfa Khobkhun, Mark Hollands and Jim Richards

Sensors 2021, 21(8), 2827; https://doi.org/10.3390/s21082827 - 16 Apr 2021

Cited by 7 | Viewed by 2917

Abstract

Difficulty in turning is prevalent in older adults and results in postural instability and risk of falling. Despite this, the mechanisms of turning problems have yet to be fully determined, and it is unclear if different speeds directly result in altered posture and [...] Read more.

Difficulty in turning is prevalent in older adults and results in postural instability and risk of falling. Despite this, the mechanisms of turning problems have yet to be fully determined, and it is unclear if different speeds directly result in altered posture and turning characteristics. The aim of this study was to identify the effects of turning speeds on whole-body coordination and to explore if these can be used to help inform fall prevention programs in older adults. Forty-two participants (21 healthy older adults and 21 younger adults) completed standing turns on level ground. Inertial Measurement Units (XSENS) were used to measure turning kinematics and stepping characteristics. Participants were randomly tasked to turn 180° at one of three speeds; fast, moderate, or slow to the left and right. Two factors mixed model analysis of variance (MM ANOVA) with post hoc pairwise comparisons were performed to assess the two groups and three turning speeds. Significant interaction effects (p < 0.05) were seen in; reorientation onset latency of head, pelvis, and feet, peak segmental angular separation, and stepping characteristics (step frequency and step size), which all changed with increasing turn speed. Repeated measures ANOVA revealed the main effects of speeds within the older adults group on those variables as well as the younger adults group. Our results suggest that turning speeds result in altered whole-body coordination and stepping behavior in older adults, which use the same temporospatial sequence as younger adults. However, some characteristics differ significantly, e.g., onset latency of segments, peak head velocity, step frequency, and step size. Therefore, the assessment of turning speeds elucidates the exact temporospatial differences between older and younger healthy adults and may help to determine some of the issues that the older population face during turning, and ultimately the altered whole-body coordination, which lead to falls. Full article

(This article belongs to the Special Issue Wearable Devices: Applications in Older Adults)

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17 pages, 550 KiB

Open AccessArticle

Evaluation of Concurrent Validity between a Smartphone Self-Test Prototype and Clinical Instruments for Balance and Leg Strength

by Linda Mansson, Pernilla Bäckman, Fredrik Öhberg, Jonas Sandlund, Jonas Selling and Marlene Sandlund

Sensors 2021, 21(5), 1765; https://doi.org/10.3390/s21051765 - 4 Mar 2021

Cited by 10 | Viewed by 3293

Abstract

The evolving use of sensors to objectively assess movements is a potentially valuable addition to clinical assessments. We have developed a new self-test application prototype, MyBalance, in the context of fall prevention aimed for use by older adults in order to independently assess [...] Read more.

The evolving use of sensors to objectively assess movements is a potentially valuable addition to clinical assessments. We have developed a new self-test application prototype, MyBalance, in the context of fall prevention aimed for use by older adults in order to independently assess balance and functional leg strength. The objective of this study was to investigate the new self-test application for concurrent validity between clinical instruments and variables collected with a smartphone. The prototype has two test procedures: static standing balance test in two positions, and leg strength test performed as a sit-to-stand test. Thirty-one older adults were assessed for balance and functional leg strength, in an outpatient physiotherapy setting, using seven different clinical assessments and three sensor-tests. The results show that clinical instruments and sensor measurements correlate to a higher degree for the smartphone leg strength test. For balance tests, only a few moderate correlations were seen in the Feet Together position and no significant correlations for the Semi Tandem Stance. This study served as a first step to develop a smartphone self-test application for older adults to assess functional balance at home. Further research is needed to test validity, reliability, and user-experience of this new self-test application. Full article

(This article belongs to the Special Issue Wearable Devices: Applications in Older Adults)

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14 pages, 2120 KiB

Open AccessArticle

Profiles of Accelerometry-Derived Physical Activity Are Related to Perceived Physical Fatigability in Older Adults

by Jessica L. Graves, Yujia (Susanna) Qiao, Kyle D. Moored, Robert M. Boudreau, Elizabeth M. Venditti, Robert T. Krafty, Eric J. Shiroma, Jaroslaw Harezlak and Nancy W. Glynn

Sensors 2021, 21(5), 1718; https://doi.org/10.3390/s21051718 - 2 Mar 2021

Cited by 6 | Viewed by 3041

Abstract

Physical activity (PA) is associated with greater fatigability in older adults; little is known about magnitude, shape, timing and variability of the entire 24-h rest–activity rhythm (RAR) associated with fatigability. We identified which features of the 24-h RAR pattern were independently and jointly [...] Read more.

Physical activity (PA) is associated with greater fatigability in older adults; little is known about magnitude, shape, timing and variability of the entire 24-h rest–activity rhythm (RAR) associated with fatigability. We identified which features of the 24-h RAR pattern were independently and jointly associated with greater perceived physical fatigability (Pittsburgh Fatigability Scale, PFS, 0–50) in older adults (n = 181, 71.3 ± 6.7 years). RARs were characterized using anti-logistic extended cosine models and 4-h intervals of PA means and standard deviations across days. A K-means clustering algorithm approach identified four profiles of RAR features: “Less Active/Robust”, “Earlier Risers”, “More Active/Robust” and “Later RAR”. Quantile regression tested associations of each RAR feature/profile on median PFS adjusted for age, sex, race, body mass index and depression symptomatology. Later rise times (up mesor; β = 1.38, p = 0.01) and timing of midpoint of activity (acrophase; β = 1.29, p = 0.01) were associated with higher PFS scores. Lower PA between 4 a.m. and 8 a.m. was associated with higher PFS scores (β = −4.50, p = 0.03). “Less Active/Robust” (β = 6.14, p = 0.01) and “Later RAR” (β = 3.53, p = 0.01) patterns were associated with higher PFS scores compared to “Earlier Risers”. Greater physical fatigability in older adults was associated with dampened, more variable, and later RARs. This work can guide development of interventions aimed at modifying RARs to reduce fatigability in older adults. Full article

(This article belongs to the Special Issue Wearable Devices: Applications in Older Adults)

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14 pages, 1528 KiB

Open AccessArticle

Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study

by Julia Wrobel, John Muschelli and Andrew Leroux

Sensors 2021, 21(4), 1545; https://doi.org/10.3390/s21041545 - 23 Feb 2021

Cited by 10 | Viewed by 3998

Abstract

The ability of individuals to engage in physical activity is a critical component of overall health and quality of life. However, there is a natural decline in physical activity associated with the aging process. Establishing normative trends of physical activity in aging populations [...] Read more.

The ability of individuals to engage in physical activity is a critical component of overall health and quality of life. However, there is a natural decline in physical activity associated with the aging process. Establishing normative trends of physical activity in aging populations is essential to developing public health guidelines and informing clinical perspectives regarding individuals’ levels of physical activity. Beyond overall quantity of physical activity, patterns regarding the timing of activity provide additional insights into latent health status. Wearable accelerometers, paired with statistical methods from functional data analysis, provide the means to estimate diurnal patterns in physical activity. To date, these methods have been only applied to study aging trends in populations based in the United States. Here, we apply curve registration and functional regression to 24 h activity profiles for 88,793 men (N = 39,255) and women (N = 49,538) ages 42–78 from the UK Biobank accelerometer study to understand how physical activity patterns vary across ages and by gender. Our analysis finds that daily patterns in both the volume of physical activity and probability of being active change with age, and that there are marked gender differences in these trends. This work represents the largest-ever population analyzed using tools of this kind, and suggest that aging trends in physical activity are reproducible in different populations across countries. Full article

(This article belongs to the Special Issue Wearable Devices: Applications in Older Adults)

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19 pages, 1753 KiB

Open AccessArticle

Quantifying the Varying Predictive Value of Physical Activity Measures Obtained from Wearable Accelerometers on All-Cause Mortality over Short to Medium Time Horizons in NHANES 2003–2006

by Lucia Tabacu, Mark Ledbetter, Andrew Leroux, Ciprian Crainiceanu and Ekaterina Smirnova

Sensors 2021, 21(1), 4; https://doi.org/10.3390/s21010004 - 22 Dec 2020

Cited by 6 | Viewed by 2773

Abstract

Physical activity measures derived from wearable accelerometers have been shown to be highly predictive of all-cause mortality. Prediction models based on traditional risk factors and accelerometry-derived physical activity measures are developed for five time horizons. The data set contains 2978 study participants between [...] Read more.

Physical activity measures derived from wearable accelerometers have been shown to be highly predictive of all-cause mortality. Prediction models based on traditional risk factors and accelerometry-derived physical activity measures are developed for five time horizons. The data set contains 2978 study participants between 50 and 85 years old with an average of 13.08 years of follow-up in the NHANES 2003–2004 and 2005–2006. Univariate and multivariate logistic regression models were fit separately for five datasets for one- to five-year all-cause mortality as outcome (number of events 46, 94, 155, 218, and 297, respectively). In univariate models the total activity count (TAC) was ranked first in all five horizons (AUC between 0.831 and 0.774) while the active to sedentary transition probability (ASTP) was ranked second for one- to four-year mortality models and fourth for the five-year all-cause mortality model (AUC between 0.825 and 0.735). In multivariate models age and ASTP were significant in all one- to five-year all-cause mortality prediction models. Physical activity measures are consistently among the top predictors, even after adjusting for demographic and lifestyle variables. Physical activity measures are strong stand-alone predictors and substantially improve the prediction performance of models based on traditional risk factors. Full article

(This article belongs to the Special Issue Wearable Devices: Applications in Older Adults)

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20 pages, 1182 KiB

Open AccessArticle

Iterative Learning Control for a Soft Exoskeleton with Hip and Knee Joint Assistance

by Chunjie Chen, Yu Zhang, Yanjie Li, Zhuo Wang, Yida Liu, Wujing Cao and Xinyu Wu

Sensors 2020, 20(15), 4333; https://doi.org/10.3390/s20154333 - 4 Aug 2020

Cited by 36 | Viewed by 7036

Abstract

Walking on different terrains leads to different biomechanics, which motivates the development of exoskeletons for assisting on walking according to the type of a terrain. The design of a lightweight soft exoskeleton that simultaneously assists multiple joints in the lower limb is presented [...] Read more.

Walking on different terrains leads to different biomechanics, which motivates the development of exoskeletons for assisting on walking according to the type of a terrain. The design of a lightweight soft exoskeleton that simultaneously assists multiple joints in the lower limb is presented in this paper. It is used to assist both hip and knee joints in a single system, the assistance force is directly applied to the hip joint flexion and the knee joint extension, while indirectly to the hip extension also. Based on the biological torque of human walking at three different slopes, a novel strategy is developed to improve the performance of assistance. A parameter optimal iterative learning control (POILC) method is introduced to reduce the error generated due to the difference between the wearing position and the biological features of the different wearers. In order to obtain the metabolic rate, three subjects walked on a treadmill, for 10 min on each terrain, at a speed of 4 km/h under both conditions of wearing and not wearing the soft exoskeleton. Results showed that the metabolic rate was decreased with the increasing slope of the terrain. The reductions in the net metabolic rate in the experiments on the downhill, flat ground, and uphill were, respectively,

9.86 %

,

12.48 %

, and

22.08 %

compared to the condition of not wearing the soft exoskeleton, where their corresponding absolute values were

0.28

W/kg,

0.72

W/kg, and

1.60

W/kg. Full article

(This article belongs to the Special Issue Wearable Devices: Applications in Older Adults)

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12 pages, 1318 KiB

Open AccessArticle

Quantifying Spatiotemporal Gait Parameters with HoloLens in Healthy Adults and People with Parkinson’s Disease: Test-Retest Reliability, Concurrent Validity, and Face Validity

by Daphne J. Geerse, Bert Coolen and Melvyn Roerdink

Sensors 2020, 20(11), 3216; https://doi.org/10.3390/s20113216 - 5 Jun 2020

Cited by 23 | Viewed by 4757

Abstract

Microsoft’s HoloLens, a mixed-reality headset, provides, besides holograms, rich position data of the head, which can be used to quantify what the wearer is doing (e.g., walking) and to parameterize such acts (e.g., speed). The aim of the current study is to determine [...] Read more.

Microsoft’s HoloLens, a mixed-reality headset, provides, besides holograms, rich position data of the head, which can be used to quantify what the wearer is doing (e.g., walking) and to parameterize such acts (e.g., speed). The aim of the current study is to determine test-retest reliability, concurrent validity, and face validity of HoloLens 1 for quantifying spatiotemporal gait parameters. This was done in a group of 23 healthy young adults (mean age 21 years) walking at slow, comfortable, and fast speeds, as well as in a group of 24 people with Parkinson’s disease (mean age 67 years) walking at comfortable speed. Walking was concurrently measured with HoloLens 1 and a previously validated markerless reference motion-registration system. We comprehensively evaluated HoloLens 1 for parameterizing walking (i.e., walking speed, step length and cadence) in terms of test-retest reliability (i.e., consistency over repetitions) and concurrent validity (i.e., between-systems agreement), using the intraclass correlation coefficient (ICC) and Bland–Altman’s bias and limits of agreement. Test-retest reliability and between-systems agreement were excellent for walking speed (ICC ≥ 0.861), step length (ICC ≥ 0.884), and cadence (ICC ≥ 0.765), with narrower between-systems than over-repetitions limits of agreement. Face validity was demonstrated with significantly different walking speeds, step lengths and cadences over walking-speed conditions. To conclude, walking speed, step length, and cadence can be reliably and validly quantified from the position data of the wearable HoloLens 1 measurement system, not only for a broad range of speeds in healthy young adults, but also for self-selected comfortable speed in people with Parkinson’s disease. Full article

(This article belongs to the Special Issue Wearable Devices: Applications in Older Adults)

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11 pages, 1113 KiB

Open AccessArticle

Gait Variability Using Waist- and Ankle-Worn Inertial Measurement Units in Healthy Older Adults

by Timo Rantalainen, Laura Karavirta, Henrikki Pirkola, Taina Rantanen and Vesa Linnamo

Sensors 2020, 20(10), 2858; https://doi.org/10.3390/s20102858 - 18 May 2020

Cited by 14 | Viewed by 3419

Abstract

Gait variability observed in step duration is predictive of impending adverse health outcomes among apparently healthy older adults and could potentially be evaluated using wearable sensors (inertial measurement units, IMU). The purpose of the present study was to establish the reliability and concurrent [...] Read more.

Gait variability observed in step duration is predictive of impending adverse health outcomes among apparently healthy older adults and could potentially be evaluated using wearable sensors (inertial measurement units, IMU). The purpose of the present study was to establish the reliability and concurrent validity of gait variability and complexity evaluated with a waist and an ankle-worn IMU. Seventeen women (age 74.8 (SD 44) years) and 10 men (73.7 (4.1) years) attended two laboratory measurement sessions a week apart. Their stride duration variability was concurrently evaluated based on a continuous 3 min walk using a force plate and a waist- and an ankle-worn IMU. Their gait complexity (multiscale sample entropy) was evaluated from the waist-worn IMU. The force plate indicated excellent stride duration variability reliability (intra-class correlation coefficient, ICC = 0.90), whereas fair to good reliability (ICC = 0.47 to 0.66) was observed from the IMUs. The IMUs exhibited poor to excellent concurrent validity in stride duration variability compared to the force plate (ICC = 0.22 to 0.93). A good to excellent reliability was observed for gait complexity in most coarseness scales (ICC = 0.60 to 0.82). A reasonable congruence with the force plate-measured stride duration variability was observed on many coarseness scales (correlation coefficient = 0.38 to 0.83). In conclusion, waist-worn IMU entropy estimates may provide a feasible indicator of gait variability among community-dwelling ambulatory older adults. Full article

(This article belongs to the Special Issue Wearable Devices: Applications in Older Adults)

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