Stair Height Research Articles

Indoor altitude estimation assisted by inertial compensation and online floor modeling

Abstract Indoor pedestrian altitude information plays a key role in such applications as emergency relief and military reconnaissance. However, height errors of inertial positioning grow without boundary because of the altitude channel divergence of the Strap-down Inertial Navigation System (SINS). This article proposes a new vertical location method based on the foot-mounted Inertial Measurement Unit (IMU) and online floor modeling. First, the number of stairs at each step is calculated after the height of each step is corrected by the error compensation model. Second, the prior height is estimated by utilizing the number of stairs and the stair height. Then, the key point library related to the floor is built online. Finally, the error correction of the vertical displacement is carried out by matching the prior height with the key point library. The gait classification shows that the accuracy based on the error compensation model can reach up to 99%. Moreover, the maximum attitude error is less than 1 m and the accumulated vertical positioning errors are eliminated completely when a pedestrian walks up and down inside a multi-story building, and all these have verified accuracy and robustness of vertical indoor positioning.

Minimal power required to ascend a flight of stairs versus actual power measured with body-fixed sensors in adults aged 19-85 years.

A good stair-climbing (SC) ability is crucial for independent living in older adults. A simple formula that estimates the mean power needed to ascend a flight of stairs in a predetermined time (i.e., total ascent duration) is easy to implement in practice, but lacks information on actual power values generated per step. The latter is possible with body-fixed sensors. This study aimed at comparing both methodologies and investigating their sensitivity to detect age-related differences. 318 participants (162 ♀; age 19-85 years) were tested on a 6-step staircase and two methodologies were used to estimate mean SC power: (1) a body-fixed sensor with automated detection of power production per step, and (2) a mathematic equation based on timed ascent duration, body mass and stair height. SC power was 210.4 W lower with formula compared to sensor, lower in women versus men and in older versus young adults (p < 0.001). The difference in SC power between sensor and formula was greater in individuals with better performance (i.e., men and young adults) (p < 0.001), indicating a ceiling effect of the formula in well-functioning and younger individuals. Likewise, ICC's between both methodologies showed poor reliability in people aged <65 years (0.087-0.363) and moderate to good reliability in people aged ≥65 years (0.453-0.780). To conclude, participants with better SC performance are able to largely overshoot the minimal power required to ascend the stairs in a certain duration. This makes the sensor more sensitive to identify early age-related differences compared to the formula.

PERCENT TIME SPENT IN AN IMPAIRED GLUCOSE STATE AND ADVERSE MUSCLE OUTCOMES: THE SWAN STUDY

Abstract Sarcopenia is common among older adults, but associations of sarcopenia and diabetes are mixed. Beyond sarcopenia, relationships between glucose levels and muscle quality and power are understudied. We examined the association of time spent in an impaired glucose state and muscle outcomes. Muscle quality and power were assessed in 2015-2016 in 1,614 women (aged 66 + 2.7 years) without diabetes at baseline. Muscle quality was the average grip strength (measured in three trials using a Jamar dynamometer) per kilogram whole-body skeletal muscle mass (measured using bioelectrical-impedance analysis). Muscle power (Watts/kilogram body weight) was assessed using a 3-cycle timed stair climb: [(body weight (N)*stair height (m))/average ascent time (sec)]/body weight (kg). Fasting blood was collected at 11 visits; glucose ≥100mg/dL was considered impaired, with percent time spent in an impaired glucose state (%IGS) calculated as:(number visits with impaired glucose)/(number visits with glucose)*100 and categorized as ≤24%(n=1,203), 25-49%(n=184), 50-74%(n=136), ≥75%(n=91). Cross-sectional multivariable linear regression models were constructed for muscle outcomes with the independent predictor %IGS, adjusting for age, menopausal status, race/ethnicity, hormone use, and height. Compared to the lowest %IGS, all other groups had significantly lower mean muscle quality (β range: -0.091, -0.110 and -0.135, respectively) with the largest difference vs. highest %IGS (β= -0.135, 95%CI:-0.192,-0.077). Similar results were observed for muscle power (β range: -0.022, -0.024 and -0.020, respectively), though the highest %IGS was not significantly different. Results suggest that a longer time in an impaired glucose state from middle to older age may be associated with lower muscle function in older women.

Traumatic Anterior Cervical Disc Herniation Presenting as Severe Dysphagia.

Due to the anatomical characteristics of the cervical spine, few cases of traumatic anterior cervical disc herniation have been reported in the literature. Here, we present a rare case of a traumatic anterior cervical disc herniation presenting as severe dysphagia. A 75-year-old male patient presented with severe dysphagia following an accident three days prior when he fell from a height of stairs. Cervical magnetic resonance (MR) imaging revealed a 1.3 × 1.0 cm extruded disc in the anterior aspect of the C4 level with the base at the C3-4 disc, which displaced the esophagus anteriorly. Esophagography revealed an extrinsic esophageal lesion that was considered to be responsible for the obstruction of the airway at the same level. He underwent a ruptured disc removal via the anterior approach. Preoperative dysphagia was resolved gradually after surgery, and he remained asymptomatic six months after surgery.

Design and Study of an AI-Powered Autonomous Stair Climbing Robot

Mobile robots are frequently utilized in the surveillance sector for both industrial and military purposes. The ability to navigate stairs is crucial for carrying out surveillance jobs like urban search and rescue operations. The research paper shows that the design methodology for a six-wheeled rover robot that can adapt to various stairs and maintain its stability based on the robot's specifications, kinematics restrictions, the maximum height, and the lowest step length needed to climb up and down the stairs is proposed. Based on a Raspberry Pi, camera, and LIDAR distance sensor, the suggested robot has the capacity to measure the stair height before starting to climb. A Convolutional Neural Networks (CNN) deep learning model is developed for the purpose of stair recognition. Additionally, stair alignment was estimated using statistical filtering on pictures and LIDAR distance reading. The robot can then decide whether it can climb the stairs or not based on its kinematics limitations and the height of the stairs as measured by our system. Result shows that our stair detection algorithm achieved an accuracy of 99.46% and a mean average precision of 99.64%. The proposed AI-Powered Robot-based stair recognition system, according to final results, effectively climbed stairs with a height range between 13 and 23 cm.

High-Precision Calibration of a Monocular-Vision-Guided Handheld Line-Structured-Light Measurement System.

Due to the advantages of simple construction, easy application and good environmental suitability, handheld structured-light measurement systems have broad application prospects in 3D measurements. Here, a monocular-vision-guided line-structured-light measurement system is developed, and the posture of the handheld device can be obtained via a specifically designed target attached to it. No more marker points need to be adhered onto the object under inspection. The key for the system calibration is to obtain the coordinate transformation matrix from the sensor to the featured target coordinate system. The mathematical model of the system is first established. Then, an improved multi-view calibration method is proposed, where a selection process for the image pairs is conducted for accuracy improvement. With this method, the maximum relative error of the measured stair heights can be reduced from 0.48% to 0.16%. The measurement results for the specific parts further verified the effectiveness of the proposed system and the calibration method.

Novel stair-shaped ground absorber for performance enhancement of solar chimney power plant

The present study investigates the use of a stair-shaped ground surface of a chimney-based solar thermal power plant (SCPP). The stair-shaped absorber is implemented to augment the efficacy of SCPP. The current investigation is conducted with a finite volume method-based solver, considering turbulent flow and solar irradiation models. Thermo-hydraulic performance, power generation, and irreversibility production of the plant are analyzed for different dimensions of the stair-shaped absorber. The present computing model is validated against the Manzaranes pilot plant with good agreement. The analysis indicates that the proposed stair-shaped absorber can improve power generation by up to ∼ 80% (compared to a flat ground absorber surface). The power generation of a classical plant can be enhanced from 51 to 92 kW. The sloped ground absorber (a limiting case with infinite steps) generates 82 kW power, which is ∼ 19% less than the stepped ground absorber surface. The role of geometric dimensions of the stair-shaped absorber is systematically examined. It reveals an increasing central height or number of steps of the absorber improves overall plant efficiency. However, the net exergy efficiency does not increase significantly with stair height, despite the rise in chimney exergy efficiency. Mathematical correlations for predicting power generation and different efficiencies are also developed. Thus, this work provides comprehensive insights into the potential of a stair-shaped absorber, and the findings suggest that the proposed ground absorber of a stair shape could be an alternative solution for improving power generation in classical SCPPs.

Obstacle detection and autonomous stair climbing of a miniature jumping robot

Miniature jumping robots (MJRs) have difficulty executing autonomous movements in unstructured environments with obstacles because of their limited perception and computing resources. This study investigates the obstacle detection and autonomous stair climbing methods for MJRs. We propose an obstacle detection method based on a combination of attitude and distance detections, as well as MJRs’ motion. A MEMS inertial sensor collects the yaw angle of the robot, and a ranging sensor senses the distance between the robot and the obstacle to estimate the size of the obstacle. We also propose an autonomous stair climbing algorithm based on the obstacle detection method. The robot can detect the height and width of stairs and its position relative to the stairs and then repeatedly jump to climb them step by step. Moreover, the height, width, and position are sent to a control terminal through a wireless sensor network to update the information regarding the MJR and stairs in a control interface. Furthermore, we conduct stair detection, modeling, and stair climbing experiments on the MJR and obtain acceptable precisions for autonomous obstacle negotiation. Thus, the proposed obstacle detection and stair climbing methods can enhance the locomotion capability of the MJR in environmental monitoring, search and rescue, etc.

Biomechanical Evaluation of Stair Ambulation Using Impedance Control on an Active Prosthesis.

Active prostheses can provide net positive work to individuals with amputation, offering more versatility across locomotion tasks than passive prostheses. However, the effect of powered joints on bilateral biomechanics has not been widely explored for ambulation modes different than level ground and treadmill walking. In this study, we present the bilateral biomechanics of stair ascent and descent with a powered knee-ankle prosthesis compared to the biomechanical profiles of able-bodied subjects at different configurations of stair height between 102 mm and 178 mm. In addition, we include reference profiles from users with passive prostheses for the nominal stair height of 152 mm to place our findings in relation to the typical solution for individuals with transfemoral amputation (TFA). We report the biomechanical profiles of kinematics, kinetics, and power, together with temporal and waveform symmetry and distribution of mechanical energy across the joints. We found that an active prosthesis provides a substantial contribution to mechanical power during stair ascent and power absorption during stair descent and gait patterns like able-bodied subjects. The active prosthesis enables step-over-step gait in stair ascent. This translates into a lower mechanical energy requirement on the intact side, with a 57% reduction of energy at the knee and 26% at the hip with respect to the passive prosthesis. For stair descent, we found a 28% reduction in the negative work done by the intact ankle. These results reflect the benefit of active prostheses, allowing the users to complete tasks more efficiently than passive legs. However, in comparison to able-bodied biomechanics, the results still differ from the ideal patterns. We discuss the limitations that explain this difference and suggest future directions for the design of impedance controllers by taking inspiration from the biological modulation of the knee moment as a function of the stair height.

The Impact Of Leg Asymmetry On Stair Climb Performance In Career Firefighters

Limb-specific regional tissue distribution can be determined using segmental body composition analyses to indicate between limb asymmetries. It is unclear how leg muscle imbalance, as measured by fat free mass, may contribute to performance on job-related tasks. PURPOSE: The purpose of this study was to examine the relationship between leg fat free mass asymmetry (FFMA) and stair climb performance (SCP) in career firefighters. METHODS: Forty-one firefighters [age: 32.3±8.2 years (20-50); stature: 178.3±7.9 cm (159.2-194.6); mass: 92.3±18.7 kg (65.1-133.2); %BF: 24.1±7.9% (9.9-39.4)] volunteered for this investigation. All participants were free of any neuromuscular, cardiovascular, or metabolic diseases, and were instructed to refrain from caffeine and smokeless tobacco for 12 hours, and vigorous lower body exercise for 48 hours. Following an 8 hour fast, %BF and leg FFM was assessed by a daily calibrated dual-energy x-ray absorptiometry. Participants were fitted with a weighted vest (22.73 kg) to simulate the load of their personal protective equipment and self-contained breathing apparatus. Performance was measured as time to completion (s) of stair assent and decent of 26 stairs (20 cm stair height) four times as fast as possible (104 total stairs). Leg FFMA was defined as ±3% difference between leg fat free mass as a cutoff point. A point biserial correlation between FFMA and SCP was performed. In addition, the relationship between FFMA and SCP, while controlling for age and %BF, was assessed. An alpha level was set a priori at 0.05. RESULTS: The point biserial correlation demonstrated that FFMA was significantly associated with SCP (r=0.519, P<0.001). Due to the large range in age and %BF, a follow-up partial correlation analysis controlling for these variables was performed and indicated a similar relationship between FFMA and SCP (r = 0.561, P<0.001). CONCLUSIONS: These findings suggest that the presence of leg FFMA is related to poorer performance on the stair climb assessment with and without accounting for age and %BF. While age is non-modifiable, maintaining good body composition (i.e., %BF and muscle symmetry) as a firefighter may help preserve performance on fire-related tasks. Strategies to improve body composition may impact SCP in firefighters.

Powered Knee and Ankle Prosthesis With Adaptive Control Enables Climbing Stairs With Different Stair Heights, Cadences, and Gait Patterns

Powered prostheses can enable individuals with above-knee amputations to ascend stairs step-over-step. To accomplish this task, available stair ascent controllers impose a pre-defined joint impedance behavior or follow a pre-programmed position trajectory. These control approaches have proved successful in the laboratory. However, they are not robust to changes in stair height or cadence, which is essential for real-world ambulation. Here we present an adaptive stair ascent controller that enables individuals with above-knee amputations to climb stairs of varying stair heights at their preferred cadence and with their preferred gait pattern. We found that modulating the prosthesis knee and ankle position as a function of the user's thigh in swing provides toe clearance for varying stair heights. In stance, modulating the torque-angle relationship as a function of the prosthesis knee position at foot contact provides sufficient torque assistance for climbing stairs of different heights. Furthermore, the proposed controller enables individuals to climb stairs at their preferred cadence and gait pattern, such as step-by-step, step-over-step, and two-steps. The proposed adaptive stair controller may improve the robustness of powered prostheses to environmental and human variance, enabling powered prostheses to more easily move from the lab to the real-world.

Subject-Independent, Biological Hip Moment Estimation During Multimodal Overground Ambulation Using Deep Learning

Estimating biological joint moments using wearable sensors could enable out-of-lab biomechanical analyses and exoskeletons that assist throughout daily life. To realize these possibilities, this study introduced a subject-independent hip moment estimator using a temporal convolutional network (TCN) and validated its performance and generalizability during multimodal ambulation. Electrogoniometer and simulated IMU data from sixteen participants walking on level ground, ramps and stairs were used to evaluate our approach when benchmarked against a fully-connected neural network, a long short-term memory network, and a baseline method (i.e., using subject-average moment curves based on ambulation mode and gait phase). Additionally, the generalizability of our approach was evaluated by testing on ground slopes, stair heights, and gait transitions withheld during model training. The TCN outperformed the benchmark approaches on the hold-out data (p < 0.05), with an average RMSE of 0.131±0.018 Nm/kg and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.880±0.030 during steady-state ambulation. When tested on the 20 leave-one-out slope and stair height conditions, the TCN significantly increased RMSE only on the steepest (+18°) incline (p < 0.05). Finally, the TCN RMSE and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> was 0.152±0.027 Nm/kg and 0.786±0.055, respectively, during mode transitions. Thus, our approach accurately estimated hip moments and generalized to unseen gait contexts using data from three wearable sensors.

Toward Generalization of Bipedal Gait Cycle During Stair Climbing Using Learning From Demonstration

Lower limb assistive exoskeletons for rehabilitation is a growing field of study. They allow people with spinal cord injuries and strokes to stand upright and walk. Exoskeleton controls research typically focuses on trajectory generation, motion control over the trajectories, and balance and stability. This work focuses on the generalization of people's joint trajectories climbing a single stair, for an exoskeleton to be developed for people of all leg lengths and be used on variable stair heights. The motion of subjects of different heights, ages, and genders climbing a single stair was collected using motion capture. By using Gaussian mixed regression and Gaussian mixed models, these trajectories were combined to build a model that generalizes individuals with different leg lengths, genders, ages and stair heights. The variability of the subject's leg length is accounted for by tracking a marker on the toe of the subject and using inverse kinematics to calculate the joint angles. The analysis in this paper shows that joint angles can be generalized to climb various stair heights. In addition, an open-source database of motion capture climbing data and an open-source framework for generating joint trajectories has been made available for public use.

A Machine Learning Strategy for Locomotion Classification and Parameter Estimation Using Fusion of Wearable Sensors

The accurate classification of ambulation modes and estimation of walking parameters is a challenging problem that is key to many applications. Knowledge of the user's state can enable rehabilitative devices to adapt to changing conditions, while in a clinical setting it can provide physicians with more detailed patient activity information. This study describes the development and optimization process of a combined locomotion mode classifier and environmental parameter estimator using machine learning and wearable sensors. A detailed analysis of the best sensor types and placements for each problem is also presented to provide device designers with information on which sensors to prioritize for their application. For this study, 15 able-bodied subjects were unilaterally instrumented with inertial measurement unit, goniometer, and electromyography sensors and data were collected for extensive ranges of level-ground, ramp, and stair walking conditions. The proposed system classifies steady state ambulation modes with 99% accuracy and ambulation mode transitions with 96% accuracy, along with estimating ramp incline within 1.25 degrees, stair height within 1.29 centimeters, and walking speed within 0.04 meters per second. Mechanical sensors (inertial measurement units, goniometers) are found to be most important for classification, while goniometers dominate ramp incline and stair height estimation, and speed estimation is performed largely with a single inertial measurement unit. The feature tables and Matlab code to replicate the study are published as supplemental materials.

A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions

We introduce a novel dataset containing 3-dimensional biomechanical and wearable sensor data from 22 able-bodied adults for multiple locomotion modes (level-ground/treadmill walking, stair ascent/descent, and ramp ascent/descent) and multiple terrain conditions of each mode (walking speed, stair height, and ramp inclination). In this paper, we present the data collection methods, explain the structure of the open dataset, and report the sensor data along with the kinematic and kinetic profiles of joint biomechanics as a function of the gait phase. This dataset offers a comprehensive source of locomotion information for the same set of subjects to motivate applications in locomotion recognition, developments in robotic assistive devices, and improvement of biomimetic controllers that better adapt to terrain conditions. With such a dataset, models for these applications can be either subject-dependent or subject-independent, allowing greater flexibility for researchers to advance the field.

Machine Learning Model Comparisons of User Independent &amp; Dependent Intent Recognition Systems for Powered Prostheses

Developing intelligent prosthetic controllers to recognize user intent across users is a challenge. Machine learning algorithms present an opportunity to develop methods for predicting user's locomotion mode. Currently, linear discriminant analysis (LDA) offers the standard solution in the state-of-the-art for subject dependent models and has been used in the development of subject independent applications. However, the performance of subject independent models differ radically from their dependent counterpart. Furthermore, most of the studies limit the evaluation to a fixed terrain with individual stair height and ramp inclination. In this study, we investigated the use of the XGBoost algorithm for developing a subject independent model across 8 individuals with transfemoral amputation. We evaluated the performance of XGBoost across different stair heights and inclination angles and found that it generalizes well across preset conditions. Our findings suggest that XGBoost offers a potential benefit for both subject independent and subject dependent algorithms outperforming LDA and NN (DEP SS Error: 2.93% ± 0.49%, DEP TS Error: 7.03% ± 0.74%, IND SS Error: 10.12% ± 3.16%, and IND TS Error: 15.78% ± 2.39%)(p <; 0.05). We were also able to show that with the inclusion of extra sensors the model performance could continually be improved in both user dependent and independent models (p <; 0.05). Our study provides valuable information for future intent recognition systems to make them more reliable across different users and common community ambulation modes.

Lower limb muscle activities and gain in balancing ability following two types of stair gait intervention in adult post-chronic stroke patients: A preliminary, randomized-controlled study.

This study aims to compare the changes in lower limb muscle activities after stair ascending and descending training at two different heights of stairs in patients with chronic stroke and to suggest a stair height which is more effective in improving the strength and balancing ability of these patients. Between November 2016 and February 2017, a total of 20 patients (14 males, 6 females; mean age 56 years; range, 52 to 61 years) with hemiparesis were included in this randomized-controlled study. The patients were randomly assigned to the 10- or 15-cm stair height group (10- and 15-cm groups, respectively; n=10 in each). Both groups received comprehensive rehabilitation therapy and additionally performed stair gait training for 30 min four times per week for a total of six weeks. Balancing abilities and the activities of the paralyzed lower limb's rectus femoris, biceps femoris, tibialis anterior, and gastrocnemius during stair ascending were measured before and after the stair gait training. During stair ascent, the 15-cm group showed significantly greater muscle activities of the rectus femoris, biceps femoris, and tibialis anterior than the 10-cm group (p<0.001, η2=0.115; p=0.001, η2=0.022; and p=0.001, η2=0.036, respectively). Our study results suggest that ascending stairs with 15-cm step height results in a greater muscle activity than with a 10-cm step height.

Foot-Mounted Pedestrian Navigation Method Based on Gait Classification for Three-Dimensional Positioning

A foot-mounted pedestrian navigation system (PNS) that uses MEMS inertial measurement units (IMUs) can track the position of a person when walking normally on two-dimensional (2D) plane. However, the positioning accuracy is decreased under complex gaits. This article proposes a pedestrian navigation method based on gait classification for three-dimensional (3D) positioning. The commonly used gaits are divided into seven types and a bidirectional long short-term memory recurrent neural network (BLSTM-RNN) is constructed as gait classifier. When the detected gaits are walking sideways or running, the step length models are built to calculate single step length. Otherwise the inertial navigation algorithm with zero velocity update (ZUPT) is used to calculate travel distance. If the pedestrian is going upstairs or downstairs, a barometer together with stair height constraint is applied to obtain altitude variation. Meanwhile, the heading drift is reduced combining zero integrated heading rate (ZIHR) method and simplified heuristic drift reduction (HDR) method. The gait classification results show that the classification accuracy of designed BLSTM-RNN reaches 99.4%. The results of mixed gaits experiment and multi-floor experiment show that the positioning errors of 2D and 3D trajectories are less than 2%.

Autonomous Pedestrian Altitude Estimation Inside a Multi-Story Building Assisted by Motion Recognition

Owing to autonomy and continuity, a pedestrian navigation system (PNS) has been widely deployed, which is based on the micro electro-mechanical system inertial measurement unit (MEMS-IMU) and the strapdown inertial navigation system (SINS). However, altitude information cannot be effectively obtained in this system by the double integral of vertical acceleration because of the altitude channel divergent of SINS. The study is aimed at improving the accuracy and robustness of altitude estimation through a novel method based on foot-mounted MEMS-IMU. More specifically, the proposed method exploits the adaptive network-based fuzzy inference system (ANFIS) to recognize vertical motion modes including horizontal, downstairs, and upstairs movements. Then, the pseudo height model based on both motion modes and the stair height is constructed for stair walking with different height. Finally, the pseudo measurements from the pseudo height model are integrated with the data from motion prediction through the extended Kalman filter (EKF). Experimental results show that the overall classification accuracy of ANFIS can reach up to 99.1%. Since ANFIS is utilized to assist height estimation, the cumulative height error accounts for about 1.2% over a total height of 44 m when a pedestrian walks up and down six floors without external facility and barometric pressure support. It is concluded that the ANFIS-based height estimation method can achieve better vertical positioning performance for PNS than the existing approaches in terms of accuracy and robustness.

Psychological Aspects of Stair Use: A Systematic Review.

Stair use, a common lifestyle activity, is a moderate-to-vigorous physical activity that, despite often being brief in duration, may contribute to psychological health. A systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) method to summarize psychological aspects related to stair use. Included studies examined at least 1 psychological outcome in relation to either objective measures of stair use, such as time or stair height, or subjective measures of, or measures related to, stair use such as perceived difficulty using stairs. A total of 22 studies met the inclusion criteria; 12 used subjective stair use measures and 10 used objective stair use measures. The limited evidence from studies using self-reports supported that (1) perceived difficulty using stairs was positively associated with increased symptoms of anxiety and depression and (2) stair use was not associated with a reduced incidence of mental illnesses such as depression, suicide, or dementia. Studies using objective measures of stair use supported that (3) elevated symptoms of anxiety and depression are negatively associated with stair use performance. Given the widespread use of stairs, there is surprisingly little data about the extent to which, and for whom, stair use influences psychological health.