Search Results (59)

Consumer-grade wrist-based wearable devices have grown in popularity among researchers to continuously collect metrics such as physical activity and heart rate. However, manufacturers rarely disclose the preprocessing sensor data algorithms, and user-generated data are typically shared leading to data governance issues. Open-source technology may address these limitations. This study evaluates the validity of the Bangle.js2 for step counting and heart rate during lab-based validation and agreement with other wearable devices (steps: Fitbit Charge 5; heart rate: Polar H10) in free-living conditions. A custom open-source application was developed to capture the sensor data from the Bangle.js2. Participants (n = 47; 25 males; 27 ± 11 years) were asked to complete a lab-based treadmill validation (3 min stages at 2, 3, 4, and 5 mph) and stair climbing procedure followed by a 24 h free-living period. The Bangle.js2 demonstrated systematic undercounting of steps at slower walking speeds with acceptable error achieved at 5 km/h. During free-living conditions, the Bangle.js2 demonstrated strong agreement with the Fitbit Charge 5 for per-minute step counting (CCC = 0.90) and total steps over 24 h (CCC = 0.96). Additionally, the Bangle.js2 demonstrated strong agreement with the Polar H10 for minute-averaged heart rate (CCC = 0.78). In conclusion, the Bangle.js2 is a valid open-source hardware and software solution for researchers interested in step counting and heart rate monitoring in free-living conditions. Full article

This study aimed to evaluate the intra- and inter-instrument variability and reliability of the Axivity AX6 accelerometer under controlled technical conditions and human motion scenarios. In the first experiment, 12 accelerometers were affixed to a vibration platform and tested at four frequencies (2.2, 3.2, 6.5, and 9.4 Hz) along three axes to assess frequency- and axis-dependent variability. In the second experiment, four AX6 accelerometers were simultaneously placed on a subject’s wrist and tested under four human motion conditions (walking at 4 km·h⁻¹ and 6 km·h⁻¹ and running at 8 km·h⁻¹ and 10 km·h⁻¹). Results demonstrated low intra- and inter-instrument variability (CVintra: 3.3–4.5%; CVinter: 6.3–7.7%) with high reliability (ICC = 0.98). Similar results were observed in human motion conditions (CVintra: 5.3–8.8%; CVinter: 7.1–10.4%), with ICC values of 0.98 for combined devices, and 0.99 for each device individually. Despite statistically significant differences (p < 0.05) between devices in human motion all conditions, the variations remained below the minimal clinically significant difference threshold. These findings indicate that under technical conditions on a vibrating platform, and within the range of typical human accelerations, the Axivity AX6 is a reliable tool for measuring accelerations representative of physical activity. However, further research is necessary to validate its performance under free-living conditions. Full article

Freezing of gait (FOG) is a motor symptom experienced by people with Parkinson’s Disease (PD) where they feel like they are glued to the floor. Accurate and continuous detection is needed for effective cueing to prevent or shorten FOG episodes. A convolutional neural network (CNN) was developed to detect FOG episodes in data recorded from an inertial measurement unit (IMU) on a PD patient’s ankle under semi-free living conditions. Data were split into two sets: one with all movements and another with walking and turning activities relevant to FOG detection. The CNN model was evaluated using five-fold cross-validation (5Fold-CV), leave-one-subject-out cross-validation (LOSO-CV), and performance metrics such as accuracy, sensitivity, precision, F1-score, and AUROC; Data from 24 PD participants were collected, excluding three with no FOG episodes. For walking and turning activities, the CNN model achieved AUROC = 0.9596 for 5Fold-CV and AUROC = 0.9275 for LOSO-CV. When all activities were included, AUROC dropped to 0.8888 for 5Fold-CV and 0.9017 for LOSO-CV; the model effectively detected FOG in relevant movement scenarios but struggled with distinguishing FOG from other inactive states like sitting and standing in semi-free-living environments. Full article

Background/Objectives: The brain-derived neurotrophic factor (BDNF) is a critical exercise-induced modulator of various neuroplasticity processes, including adult hippocampal neurogenesis. Environmental affordance for physical activity is a novel theory that aims to increase the BDNF through walking or climbing stairs, stimulated by the urban and interior environment. In a systematic review, this paper explores the association between walking, as a structured or free-living form of physical activity, and changes in the BDNF in humans with healthy locomotion. Method: A systematic review with a registered protocol, INPLASY2024110093, and following the PRISMA guidelines, includes English-language original research articles on adult and older adult human subjects who are locomotor-healthy, studies on walking as a structured exercise or free-living physical activity that is presented in a non-combined intervention, and must report changes in the BDNF as a dependent variable. The search was conducted using three databases: PubMed, Web of Science, and Scopus, resulting in 21 eligible studies. Results: This systematic review finds that the impact of walking on the BDNF is evidenced, but subject to moderate to high intensities in single bouts. At the same time, the long-term effects are yet to be fully understood, potentially due to the uptake of the BDNF for functional brain improvements, neuroplasticity processes, or muscle repair, instead of an accumulation of the BDNF itself, yet still confirm the important role of the BDNF for neurosustainability. Age and environmental factors such as heat are also found to affect the increase in the BDNF. The narrative synthesis provides elaborate explanations for understanding those complex dynamics before reaching future conclusions on the impact of walking or environmental affordance for physical activity on the changes in the BDNF concentrations. Conclusions: This systematic review highlights the potential role played by moderate- and high-intensity walking as a lifestyle intervention that can be utilised through the built environment to promote adaptive brain changes, through the sustainable regulation of the BDNF. Full article

The attractor complexity index (ACI) is a recently developed gait analysis tool based on nonlinear dynamics. This study assesses ACI’s sensitivity to attentional demands in gait control and its potential for characterizing age-related changes in gait patterns. Furthermore, we compare ACI with classical gait metrics to determine its efficacy relative to established methods. A 4 × 200 m indoor walking test with a triaxial accelerometer attached to the lower back was used to compare gait patterns of younger (N = 42) and older adults (N = 60) during normal and metronome walking. The other linear and non-linear gait metrics were movement intensity, gait regularity, local dynamic stability (maximal Lyapunov exponents), and scaling exponent (detrended fluctuation analysis). In contrast to other gait metrics, ACI demonstrated a specific sensitivity to metronome walking, with both young and old participants exhibiting altered stride interval correlations. Furthermore, there was a significant difference between the young and old groups (standardized effect size: −0.77). Additionally, older participants exhibited slower walking speeds, a reduced movement intensity, and a lower gait regularity. The ACI is likely a sensitive marker for attentional load and can effectively discriminate age-related changes in gait patterns. Its ease of measurement makes it a promising tool for gait analysis in unsupervised (free-living) conditions. Full article

Stride-to-stride fluctuations during walking reflect age-related changes in gait adaptability and are estimated with nonlinear measures that confine data collection to controlled settings. Smartphones, with their embedded accelerometers, may provide accessible gait analysis throughout the day. This study investigated age-related differences in linear and nonlinear gait measures estimated from a smartphone accelerometer (SPAcc) in an unconstrained, free-living environment. Thirteen young adults (YA) and 11 older adults (OA) walked within a shopping mall with a SPAcc placed in their front right pants pocket. The inter-stride interval, calculated as the time difference between ipsilateral heel contacts, was used for dependent measures calculations. One-way repeated-measures analysis of variance revealed significant (p < 0.05) age-related differences (mean: YA, OA) for stride-time standard deviation (0.04 s, 0.05 s) and coefficient of variation (3.47%, 4.16%), sample entropy (SaEn) scale 1 (1.70, 1.86) and scale 3 (2.12, 1.80), and statistical persistence decay (31 strides, 23 strides). The fractal scaling index was not different between groups (0.93, 0.95), but exceeded those typically found in controlled settings, suggesting an upregulation in adaptive behaviour likely to accommodate the increased challenge of free-living walking. These findings support the SPAcc as a viable telehealth instrument for remote monitoring of gait dynamics, with implications for unsupervised fall-risk assessment. Full article

The scarlet macaw (Ara macao) is a charismatic species that is native to Central and South America and commonly housed in captivity. Gaps in knowledge about these birds’ behavioral ecology in the wild hinders both in situ and ex situ management and conservation efforts for this species. We conducted seventeen days of observations of free-living scarlet macaws in two locations in Costa Rica, in February 2022, with the aims of (1) advancing our knowledge of this species’ natural behavioral ecology, and (2) generating data for comparison with captive macaws to help to inform their ex situ management (e.g., enclosure design and enrichment). Routes were walked within two locations—Bosque Escondido (BE), release area for captive-bred reintroduced individuals and no extant wild population, and Punta Leona (PL), an area of natural habitat within a resort inhabited by wild scarlet macaws—and focal interval sampling of multiple scarlet macaws in a group was used to record behavior and space use. The macaws at both locations were generally active, spent most of their time high in the tree canopy, relied on climbing to move within it, and used a wide variety of supports. The macaws at PL spent significantly more time feeding and locomoting, and less time perching, than those at BE, possibly due to differences in resource availability, rearing conditions, and age. Furthermore, the wild scarlet macaws at PL exhibited a variety of foraging strategies to acquire and manipulate food items within the tree canopy, including frequent use of their feet during manipulation. Despite limitations caused by a small sample size, a short period of observations, and differences between the two populations observed, this study provides insights into the behavioral ecology of scarlet macaws in the wild, which can be used for behavioral assessments of captive macaws while informing their ex situ management, with applications to animal welfare and captive breeding programs. Full article

Background: The automatic detection of activities of daily living (ADL) is necessary to improve long-term home-based monitoring of Parkinson’s disease (PD) symptoms. While most body-worn sensor algorithms for ADL detection were developed using laboratory research systems covering full-body kinematics, it is now crucial to achieve ADL detection using a single body-worn sensor that remains commercially available and affordable for ecological use. Aim: to detect and segment Walking, Turning, Sitting-down, and Standing-up activities of patients with PD using a Smartwatch positioned at the ankle. Method: Twenty-two patients living with PD performed a Timed Up and Go (TUG) task three times before engaging in cleaning ADL in a simulated free-living environment during a 3 min trial. Accelerations and angular velocities of the right or left ankle were recorded in three dimensions using a Smartwatch. The TUG task was used to develop detection algorithms for Walking, Turning, Sitting-down, and Standing-up, while the 3 min trial in the free-living environment was used to test and validate these algorithms. Sensitivity, specificity, and F-scores were calculated based on a manual segmentation of ADL. Results: Sensitivity, specificity, and F-scores were 96.5%, 94.7%, and 96.0% for Walking; 90.0%, 93.6%, and 91.7% for Turning; 57.5%, 70.5%, and 52.3% for Sitting-down; and 57.5%, 72.9%, and 54.1% for Standing-up. The median of time difference between the manual and automatic segmentation was 1.31 s for Walking, 0.71 s for Turning, 2.75 s for Sitting-down, and 2.35 s for Standing-up. Conclusion: The results of this study demonstrate that segmenting ADL to characterize the mobility of people with PD based on a single Smartwatch can be comparable to manual segmentation while requiring significantly less time. While Walking and Turning were well detected, Sitting-down and Standing-up will require further investigation to develop better algorithms. Nonetheless, these achievements increase the odds of success in implementing wearable technologies for PD monitoring in ecological environments. Full article

Falls are a major concern for people with Parkinson’s disease (PwPD), but accurately assessing real-world fall risk beyond the clinic is challenging. Contemporary technologies could enable the capture of objective and high-resolution data to better inform fall risk through measurement of everyday factors (e.g., obstacles) that contribute to falls. Wearable inertial measurement units (IMUs) capture objective high-resolution walking/gait data in all environments but are limited by not providing absolute clarity on contextual information (i.e., obstacles) that could greatly influence how gait is interpreted. Video-based data could compliment IMU-based data for a comprehensive free-living fall risk assessment. The objective of this study was twofold. First, pilot work was conducted to propose a novel artificial intelligence (AI) algorithm for use with wearable video-based eye-tracking glasses to compliment IMU gait data in order to better inform free-living fall risk in PwPD. The suggested approach (based on a fine-tuned You Only Look Once version 8 (YOLOv8) object detection algorithm) can accurately detect and contextualize objects (mAP50 = 0.81) in the environment while also providing insights into where the PwPD is looking, which could better inform fall risk. Second, we investigated the perceptions of PwPD via a focus group discussion regarding the adoption of video technologies and AI during their everyday lives to better inform their own fall risk. This second aspect of the study is important as, traditionally, there may be clinical and patient apprehension due to ethical and privacy concerns on the use of wearable cameras to capture real-world video. Thematic content analysis was used to analyse transcripts and develop core themes and categories. Here, PwPD agreed on ergonomically designed wearable video-based glasses as an optimal mode of video data capture, ensuring discreteness and negating any public stigma on the use of research-style equipment. PwPD also emphasized the need for control in AI-assisted data processing to uphold privacy, which could overcome concerns with the adoption of video to better inform IMU-based gait and free-living fall risk. Contemporary technologies (wearable video glasses and AI) can provide a holistic approach to fall risk that PwPD recognise as helpful and safe to use. Full article

Instrumenting the six-minute walk test (6MWT) adds information about gait quality and insight into fall risk. Being physically active and preserving multi-directional stepping abilities are also important for fall risk reduction. This analysis investigated the relationship of gait quality during the 6MWT with physical functioning and physical activity. Twenty-one veterans (62.2 ± 6.4 years) completed the four square step test (FSST) multi-directional stepping assessment, a gait speed assessment, health questionnaires, and the accelerometer-instrumented 6MWT. An activity monitor worn at home captured free-living physical activity. Gait measures were not significantly different between minutes of the 6MWT. However, participants with greater increases in stride time (ρ = −0.594, p < 0.01) and stance time (ρ = −0.679, p < 0.01) during the 6MWT reported lower physical functioning. Neither physical activity nor sedentary time were related to 6MWT gait quality. Participants exploring a larger range in stride time variability (ρ = 0.614, p < 0.01) and stance time variability (ρ = 0.498, p < 0.05) during the 6MWT required more time to complete the FSST. Participants needing at least 15 s to complete the FSST meaningfully differed from those completing the FSST more quickly on all gait measures studied. Instrumenting the 6MWT helps detect ranges of gait performance and provides insight into functional limitations missed with uninstrumented administration. Established FSST cut points identify aging adults with poorer gait quality. Full article

Spatial navigation ability is essential for independent living, and it relies on complex cognitive and motor processes that are vulnerable to decline in persons with multiple sclerosis (pwMS). The role of mobility in the physical act of navigation has been well documented; however, its association with cognitive processing that supports efficient navigation and recall of the environment is unknown. This study examined the relation between clinical mobility function and spatial navigation ability in pwMS. In a clinical sample of 43 individuals with relapsing-remitting MS (M_PDDS = 2; age 25–67 years), we assessed spatial navigation ability in a virtual Morris water maze that allowed for active search by controlling a joystick while seated at a computer, and subsequent free recall of environment details. Individuals with worse mobility (measured by slower forward and backward walking) traveled less efficient virtual navigation routes to the goal location and recalled fewer accurate details of the environment. A stratified analysis by disability revealed moderate–strong correlations for those with a low level of disability, and effects were attenuated in individuals with a high level of disability. Given that the virtual navigation task was performed while seated, evidence of any correlation with mobility suggests differences in navigation ability that cannot be ascribed to general walking impairment, and instead suggests a role for mobility impairment to modify cognitive processing supporting navigation in pwMS. Full article

Moving is an indispensable component of travelling. This paper discusses the experiences of persons with disabilities when moving around cities on foot or wheels, based on research conducted during the EU-funded project TRIPS. Findings comprise participants’ vignettes from 49 interviews in seven European cities, views on smart assistive technologies (e.g., Augmented Reality) from a pan-European quantitative survey, and design concepts related to walking based on a co-creation workshop that actively engaged persons with various types of disabilities in ideation. Findings suggest that people need reliable and clear wayfaring information on accessible travel routes featuring the coordinated design of streets, pavement, stops, stations, and vehicles to ensure seamless, step-free, and obstacle-free access, as well as disability-sensitive management of disruptions such as maintenance works, for example. Findings also suggest that users are open to using any assistive technology that can enable them to live more independently, assuming it is accessible, and are keen to co-innovate. Finally, we make recommendations for policy changes that can facilitate the redesign of urban infrastructure to make cities more accessible for people with disabilities and drive structural changes in urban planning. Full article

All new physical behaviour measurement devices should be assessed for compatibility with previous devices. Agreement was assessed between the activPAL4^TM and activPAL3^TM physical behavior monitors within a laboratory and a multi-day free-living context. Healthy children aged 6–12 years performed standardised (sitting, standing, stepping) (12 min) and non-standardised (6 min) activities in a laboratory and a multi-day (median 3 days) free-living assessment whilst wearing both monitors. Agreement was assessed using Bland–Altman plots, sensitivity, and the positive predictive value (PPV). There were 15 children (7M/8F, 8.4 ± 1.8 years old) recruited. For the laboratory-based standardised activities, sitting time, stepping time, and fast walking/jogging step count were all within ±5% agreement. However, the activPAL4^TM standing time was lower (−6.4%) and normal speed walking step count higher (+7.8%) than those of the activPAL3^TM. For non-standardised activities, a higher step count was recorded by the activPAL4^TM (+4.9%). The standardised activity sensitivity and PPV were all >90%, but the non-standardised activity values were lower. For free-living agreement, the standing time was lower (−7.6%) and step count higher (all steps + 2.2%, steps with cadence >100 step/min + 6.6%) for the activPAL4^TM than the activPAL3^TM. This study highlights differences in outcomes as determined by the activPAL4^TM and activPAL3^TM, which should be considered when comparing outcomes between studies. Full article

Urban planning and transportation policies are vital to creating sustainable and liveable cities. Transit-orientated development (TOD) has emerged as a prominent approach that emphasises the establishment of neighbourhoods with convenient access to public transportation, thereby promoting car-free lifestyles. This research investigates the connection between proximity to transit stations and local living habits in Auckland, New Zealand, which is a car-dependent city aiming to transition to a sustainable TOD model. We use geolocational data from mobile phones to measure the daily mobility patterns of residents living within a 15 min walking distance of various transit stations. Employing ordinary least squares (OLS) regression, we analyse the correlation between residents’ average travel distances and individual mobility, considering different station contexts. We aim to determine whether individuals living near transit stations are more inclined to participate in local activities and make a higher proportion of short-distance trips. The results illustrate that approximately 54% of the residents show dominant localised mobility patterns. Living near a station is significantly associated with shorter annual travel distances, although this trend varies by area. Notably, only about 16 of the 34 stations studied indicate that their local residents predominantly engage in ‘local’ travel patterns. Rural stations show less correlation, likely due to poor infrastructure and limited walkability. This study underscores the vital role of proximity to transit stations in promoting sustainable mobility. It serves as a foundational guide for urban planners and designers to make informed decisions that improve the built environment and optimise land use. Full article

Fall risk assessment (FRA) helps clinicians make decisions about the best preventative measures to lower the risk of falls by identifying the different risks that are specific to an individual. With the development of wearable technologies such as inertial measurement units (IMUs), several free-living FRA methods based on fall predictors derived from IMU-based data have been introduced. The performance of such methods could be improved by increasing awareness of the individuals’ walking environment. This study aims to introduce and analyze a 25-layer convolutional neural network model for classifying nine walking surface conditions using IMU-based gait data, providing a basis for environment-aware FRAs. A database containing data collected from thirty participants who wore six IMU sensors while walking on nine surface conditions was employed. A systematic analysis was conducted to determine the effects of gait signals (acceleration, magnetic field, and rate of turn), sensor placement, and signal segment size on the method’s performance. Accuracies of 0.935 and 0.969 were achieved using a single and dual sensor, respectively, reaching an accuracy of 0.971 in the best-case scenario with optimal settings. The findings and analysis can help to develop more reliable and interpretable fall predictors, eventually leading to environment-aware FRA methods. Full article