Joint angle estimation during shoulder abduction exercise using contactless technology.

BACKGROUND: Tele-rehabilitation, also known as tele-rehab, uses communication technologies to provide rehabilitation services from a distance. The COVID-19 pandemic has highlighted the importance of tele-rehab, where the in-person visits declined and the demand for remote healthcare rises. Tele-rehab offers enhanced accessibility, convenience, cost-effectiveness, flexibility, care quality, continuity, and communication. However, the current systems are often not able to perform a comprehensive movement analysis. To address this, we propose and validate a novel approach using depth technology and skeleton tracking algorithms. METHODS: Our data involved 14 participants (8 females, 6 males) performing shoulder abduction exercises. We collected depth videos from an LiDAR camera and motion data from a Motion Capture (Mocap) system as our ground truth. The data were collected at distances of 2 m, 2.5 m, and 3.5 m from the LiDAR sensor for both arms. Our innovative approach integrates LiDAR with the Cubemos and Mediapipe skeleton tracking frameworks, enabling the assessment of 3D joint angles. We validated the system by comparing the estimated joint angles versus Mocap outputs. Personalized calibration was applied using various regression models to enhance the accuracy of the joint angle calculations. RESULTS: The Cubemos skeleton tracking system outperformed Mediapipe in joint angle estimation with higher accuracy and fewer errors. The proposed system showed a strong correlation with Mocap results, although some deviations were present due to noise. Precision decreased as the distance from the camera increased. Calibration significantly improved performance. Linear regression models consistently outperformed nonlinear models, especially at shorter distances. CONCLUSION: This study showcases the potential of a marker-less system, to proficiently track body joints and upper-limb angles. Signals from the proposed system and the Mocap system exhibited robust correlation, with Mean Absolute Errors (MAEs) consistently below [Formula: see text]. LiDAR's depth feature enabled accurate computation of in-depth angles beyond the reach of traditional RGB cameras. Altogether, this emphasizes the depth-based system's potential for precise joint tracking and angle calculation in tele-rehab applications.

A novel in-bed body posture monitoring for decubitus ulcer prevention using body pressure distribution mapping.

BACKGROUND: Decubitus ulcers are prevalent among the aging population due to a gradual decline in their overall health, such as nutrition, mental health, and mobility, resulting in injury to the skin and tissue. The most common technique to prevent these ulcers is through frequent repositioning to redistribute body pressures. Therefore, the main goal of this study is to facilitate the timely repositioning of patients through the use of a pressure mat to identify in-bed postures in various sleep environments. Pressure data were collected from 10 healthy participants lying down on a pressure mat in 19 various in-bed postures, correlating to the supine, prone, right-side, and left-side classes. In addition, pressure data were collected from participants sitting at the edge of the bed as well as an empty bed. Each participant was asked to lie in these 19 postures in three distinct testing environments: a hospital bed, a home bed, and a home bed with a foam mattress topper. To categorize each posture into its respective class, the pre-trained 2D ResNet-18 CNN and the pre-trained Inflated 3D CNN algorithms were trained and validated using image and video pressure mapped data, respectively. RESULTS: The ResNet-18 and Inflated 3D CNN algorithms were validated using leave-one-subject-out (LOSO) and leave-one-environment-out (LOEO) cross-validation techniques. LOSO provided an average accuracy of 92.07% ± 5.72% and 82.22% ± 8.50%, for the ResNet-18 and Inflated 3D CNN algorithms, respectively. Contrastingly, LOEO provided a reduced average accuracy of 85.37% ± 14.38% and 77.79% ± 9.76%, for the ResNet-18 and Inflated 3D CNN algorithms, respectively. CONCLUSION: These pilot results indicate that the proposed algorithms can accurately distinguish between in-bed postures, on unseen participant data as well as unseen mattress environment data. The proposed algorithms can establish the basis of a decubitus ulcer prevention platform that can be applied to various sleeping environments. To the best of our knowledge, the impact of mattress stiffness has not been considered in previous studies regarding in-bed posture monitoring.

Design and Validation of Vision-Based Exercise Biofeedback for Tele-Rehabilitation.

Tele-rehabilitation has the potential to considerably change the way patients are monitored from their homes during the care process, by providing equitable access without the need to travel to rehab centers or shoulder the high cost of personal in-home services. Developing a tele-rehab platform with the capability of automating exercise guidance is likely to have a significant impact on rehabilitation outcomes. In this paper, a new vision-based biofeedback system is designed and validated to identify the quality of performed exercises. This new system will help patients to refine their movements to get the most out of their plan of care. An open dataset was used, which consisted of data from 30 participants performing nine different exercises. Each exercise was labeled as "Correctly" or "Incorrectly" executed by five clinicians. We used a pre-trained 3D Convolution Neural Network (3D-CNN) to design our biofeedback system. The proposed system achieved average accuracy values of 90.57% ± 9.17% and 83.78% ± 7.63% using 10-Fold and Leave-One-Subject-Out (LOSO) cross validation, respectively. In addition, we obtained average F1-scores of 71.78% ± 5.68% using 10-Fold and 60.64% ± 21.3% using LOSO validation. The proposed 3D-CNN was able to classify the rehabilitation videos and feedback on the quality of exercises to help users modify their movement patterns.

Automated Fluid Intake Detection Using RGB Videos.

Dehydration is a common, serious issue among older adults. It is important to drink fluid to prevent dehydration and the complications that come with it. As many older adults forget to drink regularly, there is a need for an automated approach, tracking intake throughout the day with limited user interaction. The current literature has used vision-based approaches with deep learning models to detect drink events; however, most use static frames (2D networks) in a lab-based setting, only performing eating and drinking. This study proposes a 3D convolutional neural network using video segments to detect drinking events. In this preliminary study, we collected data from 9 participants in a home simulated environment performing daily activities as well as eating and drinking from various containers to create a robust environment and dataset. Using state-of-the-art deep learning models, we trained our CNN using both static images and video segments to compare the results. The 3D model attained higher performance (compared to 2D CNN) with F1 scores of 93.7% and 84.2% using 10-fold and leave-one-subject-out cross-validations, respectively.

Quantifying Mobility Scooter Performance in Winter Environments.

OBJECTIVES: To quantify mobility scooter performance when traversing snow, ice, and concrete in cold temperatures and to explore possible performance improvements with scooter winter tires. DESIGN: Cross-sectional. SETTING: Hospital-based research institute. PARTICIPANTS: Two drivers (50 and 100 kg) tested 8 scooter models (N=8). Two mobility scooters were used for winter tire testing. INTERVENTIONS: Scooters were tested on 3 different conditions in a random sequence (concrete, 2.5-cm depth snow, bare ice). Ramp ascent and descent, as well as right-angle cornering up to a maximum of 10° slopes on winter conditions, were observed. Winter tire testing used the same slopes with 2 scooters on bare and melting ice surfaces. MAIN OUTCOME MEASURES: Maximum achievable angle (MAA) and tire traction loss for ramp ascent and descent performance. The ability to steer around a corner on the ramp. RESULTS: All scooters underperformed in winter conditions, specifically when traversing snow- and ice-covered slopes (χ2 [2, N=8]=13.87-15.55, P<.001) and corners (χ2 [2, N=8]=12.25, P<.01). Half of the scooters we tested were unable to climb a 1:12 grade (4.8°) snow-covered slope without losing traction. All but 1 failed to ascend an ice-covered 1:12 grade (4.8°) slope. Performance was even more unsatisfactory for the forward downslopes on both snow and ice. Winter tires enhanced the MAA, permitting 1:12 (4.8°) slope ascent on ice. CONCLUSIONS: Mobility scooters need to be designed with winter months in mind. Our findings showed that Americans with Disabilities Act-compliant built environments, such as curb ramps that conform to a 1:12 (4.8°) slope, become treacherous or impassible to mobility scooter users when covered in ice or snow. Scooter manufacturers should consider providing winter tires as optional accessories in regions that experience ice and snow accumulation. Additional testing/standards need to be established to evaluate winter mobility scooter performance further.

Design of a Novel Wearable System for Foot Clearance Estimation.

Trip-related falls are one of the major causes of injury among seniors in Canada and can be attributable to an inadequate Minimum Toe Clearance (MTC). Currently, motion capture systems are the gold standard for measuring MTC; however, they are expensive and have a restricted operating area. In this paper, a novel wearable system is proposed that can estimate different foot clearance parameters accurately using only two Time-of-Flight (ToF) sensors located at the toe and heel of the shoe. A small-scale preliminary study was conducted to investigate the feasibility of foot clearance estimation using the proposed wearable system. We recruited ten young, healthy females to walk at three self-selected speeds (normal, slow, and fast) while wearing the system. Our data analysis showed an average correlation coefficient of 0.94, 0.94, 0.92 for the normal, slow, and fast speed, respectively, when comparing the ToF signals with motion capture. The ANOVA analysis confirmed these results further by revealing no statistically significant differences between the ToF signals and motion capture data for most of the gait parameters after applying the newly proposed foot angle and offset compensation. In addition, the proposed system can measure the MTC with an average Mean Error (ME) of -0.08 ± 3.69 mm, -0.12 ± 4.25 mm, and -0.10 ± 6.57 mm for normal, slow, and fast walking speeds, respectively. The proposed affordable wearable system has the potential to perform real-time MTC estimation and contribute to future work focused on minimizing tripping risks.

Influential Factors in Remote Monitoring of Heart Failure Patients: A Review of the Literature and Direction for Future Research.

With new advances in technology, remote monitoring of heart failure (HF) patients has become increasingly prevalent and has the potential to greatly enhance the outcome of care. Many studies have focused on implementing systems for the management of HF by analyzing physiological signals for the early detection of HF decompensation. This paper reviews recent literature exploring significant physiological variables, compares their reliability in predicting HF-related events, and examines the findings according to the monitored variables used such as body weight, bio-impedance, blood pressure, heart rate, and respiration rate. The reviewed studies identified correlations between the monitored variables and the number of alarms, HF-related events, and/or readmission rates. It was observed that the most promising results came from studies that used a combination of multiple parameters, compared to using an individual variable. The main challenges discussed include inaccurate data collection leading to contradictory outcomes from different studies, compliance with daily monitoring, and consideration of additional factors such as physical activity and diet. The findings demonstrate the need for a shared remote monitoring platform which can lead to a significant reduction of false alarms and help in collecting reliable data from the patients for clinical use especially for the prevention of cardiac events.

Deep Neural Network for Slip Detection on Ice Surface.

Slip-induced falls are among the most common causes of major occupational injuries and economic loss in Canada. Identifying the risk factors associated with slip events is key to developing preventive solutions to reduce falls. One factor is the slip-resistance quality of footwear, which is fundamental to reducing the number of falls. Measuring footwear slip resistance with the recently developed Maximum Achievable Angle (MAA) test requires a trained researcher to identify slip events in a simulated winter environment. The human capacity for information processing is limited and human error is natural, especially in a cold environment. Therefore, to remove conflicts associated with human errors, in this paper a deep three-dimensional convolutional neural network is proposed to detect the slips in real-time. The model has been trained by a new dataset that includes data from 18 different participants with various clothing, footwear, walking directions, inclined angles, and surface types. The model was evaluated on three types of slips: Maxi-slip, midi-slip, and mini-slip. This classification is based on the slip perception and recovery of the participants. The model was evaluated based on both 5-fold and Leave-One-Subject-Out (LOSO) cross validation. The best accuracy of 97% was achieved when identifying the maxi-slips. The minimum accuracy of 77% was achieved when classifying the no-slip and mini-slip trials. The overall slip detection accuracy was 86% with sensitivity and specificity of 81% and 91%, respectively. The overall accuracy dropped by about 2% in LOSO cross validation. The proposed slip detection algorithm is not only beneficial for footwear manufactures to improve their footwear slip resistance quality, but it also has other potential applications, such as improving the slip resistance properties of flooring in healthcare facilities, commercial kitchens, and oil drilling platforms.

Challenges to Human Drivers in Increasingly Automated Vehicles.

OBJECTIVE: We examine the relationships between contemporary progress in on-road vehicle automation and its coherence with an envisioned "autopia" (automobile utopia) whereby the vehicle operation task is removed from all direct human control. BACKGROUND: The progressive automation of on-road vehicles toward a completely driverless state is determined by the integration of technological advances into the private automobile market; improvements in transportation infrastructure and systems efficiencies; and the vision of future driving as a crash-free enterprise. While there are many challenges to address with respect to automated vehicles concerning the remaining driver role, a considerable amount of technology is already present in vehicles and is advancing rapidly. METHODS: A multidisciplinary team of experts met to discuss the most critical challenges in the changing role of the driver, and associated safety issues, during the transitional phase of vehicle automation where human drivers continue to have an important but truncated role in monitoring and supervising vehicle operations. RESULTS: The group endorsed that vehicle automation is an important application of information technology, not only because of its impact on transportation efficiency, but also because road transport is a life critical system in which failures result in deaths and injuries. Five critical challenges were identified: driver independence and mobility, driver acceptance and trust, failure management, third-party testing, and political support. CONCLUSION: Vehicle automation is not technical innovation alone, but is a social as much as a technological revolution consisting of both attendant costs and concomitant benefits.

How a diverse research ecosystem has generated new rehabilitation technologies: Review of NIDILRR's Rehabilitation Engineering Research Centers.

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a "total approach to rehabilitation", combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970's, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program.

Slip resistance of winter footwear on snow and ice measured using maximum achievable incline.

Protective footwear is necessary for preventing injurious slips and falls in winter conditions. Valid methods for assessing footwear slip resistance on winter surfaces are needed in order to evaluate footwear and outsole designs. The purpose of this study was to utilise a method of testing winter footwear that was ecologically valid in terms of involving actual human testers walking on realistic winter surfaces to produce objective measures of slip resistance. During the experiment, eight participants tested six styles of footwear on wet ice, on dry ice, and on dry ice after walking over soft snow. Slip resistance was measured by determining the maximum incline angles participants were able to walk up and down in each footwear-surface combination. The results indicated that testing on a variety of surfaces is necessary for establishing winter footwear performance and that standard mechanical bench tests for footwear slip resistance do not adequately reflect actual performance. Practitioner Summary: Existing standardised methods for measuring footwear slip resistance lack validation on winter surfaces. By determining the maximum inclines participants could walk up and down slopes of wet ice, dry ice, and ice with snow, in a range of footwear, an ecologically valid test for measuring winter footwear performance was established.

The Evaluation of Vertical Pole Configuration and Location on Assisting the Sit-to-Stand Movement in Older Adults with Mobility Limitations.

Grab-bars and transfer poles are common sit-to-stand aids for mobility limited older adults. This study investigated differences in kinetics and kinematics to characterize the lower-limb strength and dynamic balance requirements across different pole configurations and positions in nine mobility limited older adults. Poles were varied by location (near and far) and configuration (single vertical pole, double vertical poles, vertical pole with a horizontal bar). Results indicated that the far pole condition resulted in increased trunk (p < 0.001) and hip flexion (p < 0.01 and < 0.0001 for contralateral and ipsilateral sides, respectively), and a reduced peak vertical force applied to the pole (p < 0.001). Peak extension moments at the hip and knee were unchanged, and, therefore, pole position had no effect on task demands. Placing the pole unilaterally introduced a small kinetic asymmetry, which significantly increased peak knee extension moments on the ipsilateral side (p < 0.05). Finally, dynamic balance was relatively unchanged across pole conditions. These findings offer novel insight into pole use and the effect of varying pole location and configuration in a sample of older adults with mobility impairment, and provide the basis for future work.

Automated monitoring: a potential solution for achieving sustainable improvement in hand hygiene practices.

Adequate hand hygiene is often considered as the most effective method of reducing the rates of hospital-acquired infections, which are one of the major causes of increased cost, morbidity, and mortality in healthcare. Electronic monitoring technologies provide a promising direction for achieving sustainable hand hygiene improvement by introducing the elements of automated feedback and creating the possibility to automatically collect individual hand hygiene performance data. The results of the multiphase testing of an automated hand hygiene reminding and monitoring system installed in a complex continuing care setting are presented. The study included a baseline Phase 1, with the system performing automated data collection only, a preintervention Phase 2 with hand hygiene status indicator enabled, two intervention Phases 3 and 4 with the system generating hand hygiene reminding signals and periodic performance feedback sessions provided, and a postintervention Phase 5 with only hand hygiene status indicator enabled and no feedback sessions provided. A significant increase in hand hygiene performance observed during the first intervention Phase 3 was sustained over the second intervention Phase 4, with the postintervention phase also indicating higher hand hygiene activity rates compared with the preintervention and baseline phases. The overall trends observed during the multiphase testing, the factors affecting acceptability of the automated hand hygiene monitoring system, and various strategies of technology deployment are discussed.

A novel wearable biofeedback system to prevent trip-related falls.

Real-time gait monitoring of older adults and gait-impaired individuals while providing real-time biofeedback has the potential to help reduce trip-related falls. A low or unsuccessful Minimum Toe Clearance (MTC) is considered a predictor of tripping risk. Thus, increasing the MTC can be a key component in minimizing the likelihood of tripping. This paper discusses a proof-of-concept wearable system that estimates the MTC in real-time using two Time-of-Flight (ToF) sensors and provides auditory biofeedback to alert users if they have a low MTC during everyday walking activities. Ten healthy female adults were asked to perform two experiments: 1) walk at a predetermined speed to evaluate the proposed real-time MTC detection algorithm, and 2) walk in four conditions: baseline, biofeedback with no distraction, biofeedback with distraction 1 (talking on the phone), and biofeedback with distraction 2 (playing a simple mobile game). The average MTC values were significantly greater during all feedback conditions than the baseline, indicating that the proposed system could successfully warn users to increase their MTC in real-time.

Machine learning prediction of footwear slip resistance on glycerol-contaminated surfaces: A pilot study.

Slippery surfaces due to oil spills pose a significant risk in various environments, including industrial workplaces, kitchens, garages, and outdoor areas. These situations can lead to accidents and falls, resulting in injuries that range from minor bruises to severe fractures or head trauma. To mitigate such risks, the use of slip resistant footwear plays a crucial role. In this study, we aimed to develop an Artificial Intelligence model capable of classifying footwear as having either high or low slip resistance based on the geometric characteristics and material parameters of their outsoles. Our model was trained on a unique dataset comprising images of 37 indoor work footwear outsoles made of rubber. To evaluate the slip resistant property of the footwear, all samples were tested using a cart-type friction measurement device, and the static and dynamic Coefficient of Frictions (COFs) of each outsole was determined on a glycerol-contaminated surface. Machine learning techniques were implemented, and a classification model was developed to determine high and low slip resistant footwear. Among the various models evaluated, the Support Vector Classifier (SVC) obtained the best results. This model achieved an accuracy of 0.68 ± 0.15 and an F1-score of 0.68 ± 0.20. Our results indicate that the proposed model effectively yet modestly identified outsoles with high and low slip resistance. This model is the first step in developing a model that footwear manufacturers can utilize to enhance product quality and reduce slip and fall incidents.

Aging and the use of pedestrian facilities in winter-the need for improved design and better technology.

Walking outdoors is often difficult or impossible for many seniors and people with disabilities during winter. We present a novel approach for conducting winter accessibility evaluations of commonly used pedestrian facilities, including sidewalks, street crossings, curb ramps (curb cuts and dropped curbs), outdoor stairs and ramps, building and transit entrances, bus stops, and driveways. A total of 183 individuals, aged 18-85 completed our survey. The results show that cold weather itself had little impact on the frequency of outdoor excursions among middle-aged and older adults while the presence of snow and/or ice on the ground noticeably kept people, especially older adults at home. The survey found that the key elements decreasing winter accessibility were icy sidewalks and puddles at street crossings and curb ramps. While communities have recognized the need to improve snow and ice removal, little attention has been paid to curb ramp design which is especially ineffective in winter when the bottom of the ramps pool with rain, snow, and ice, making it hazardous and inaccessible to nearly all users. We conclude that investigations of alternative designs of curb ramp are needed.

A system for portable sleep apnea diagnosis using an embedded data capturing module.

UNLABELLED: Sleep apnea (SA) is a very common disease with serious health consequences, yet is very under-diagnosed, partially because of the high cost and limited accessibility of in-laboratory polysomnography (PSG). The purpose of this work is to introduce a newly developed portable system for the diagnosis of SA at home that is both reliable and easy to use. The system includes personal devices for recording breath sounds and airflow during sleep and diagnostic algorithms to process the recorded data. The data capturing device consists of a wearable face frame with an embedded electronic module featuring a unidirectional microphone, a differential microphone preamplifier, a microcontroller with an onboard differential analogue to digital converter, and a microSD memory card. The device provides continuous data capturing for 8 h. Upon completion of the recording session, the memory card is returned to a location for acoustic analysis. We recruited 49 subjects who used the device independently at home, after which each subject answered a usability questionnaire. Random data samples were selected to measure the signal-to-noise ratio (SNR) as a gauge of hardware functionality. A subset of 11 subjects used the device on 2 different nights and their results were compared to examine diagnostic reproducibility. Independent of those, system's performance was evaluated against PSG in the lab environment in 32 subject. The overall success rate of applying the device in un-attended settings was 94 % and the overall rating for ease-of-use was 'excellent'. Signal examination showed excellent capturing of breath sounds with an average SNR of 31.7 dB. Nine of the 11 (82 %) subjects had equivalent results on both nights, which is consistent with reported inter-night variability. The system showed 96 % correlation with simultaneously performed in-lab PSG. CONCLUSION: Our results suggest excellent usability and performance of this system and provide a strong rationale to further improve it and test its robustness in a larger study.

The effect of automated monitoring and real-time prompting on nurses' hand hygiene performance.

Adequate hand hygiene compliance by healthcare staff is considered an effective method to reduce hospital-acquired infections. The electronic system developed at Toronto Rehabilitation Institute automatically detects hand hygiene opportunities and records hand hygiene actions. It includes an optional visual hand hygiene status indication, generates real-time hand hygiene prompting signals, and enables automated monitoring of individual and aggregated hand hygiene performance. The system was installed on a complex continuous care unit at the entrance to 17 patient rooms and a utility room. A total of 93 alcohol gel and soap dispensers were instrumented and 14 nurses were provided with the personal wearable electronic monitors. The study included three phases with the system operating in three different modes: (1) an inactive mode during the first phase when hand hygiene opportunities and hand hygiene actions were recorded but prompting and visual indication functions were disabled, (2) only hand hygiene status indicators were enabled during the second phase, and (3) both hand hygiene status and real-time hand hygiene prompting signals were enabled during the third phase. Data collection was performed automatically during all of the three phases. The system indicated significantly higher hand hygiene activity rates and compliance during the third phase, with both hand hygiene indication and real-time prompting functions enabled. To increase the efficacy of the technology, its use was supplemented with individual performance reviews of the automatically collected data.

Development and Evaluation of BLE-Based Room-Level Localization to Improve Hand Hygiene Performance Estimation.

Hand hygiene is one of the most effective ways to prevent infection transmission. However, current electronic monitoring systems are not able to identify adherence to all hand hygiene (HH) guidelines. Location information can play a major role in enhancing HH monitoring resolution. This paper proposes a BLE-based solution to localize healthcare workers inside the patient room. Localization accuracy was evaluated using one to four beacons in a binary (entrance/proximal patient zone) or multiclass (entrance/sink/right side of the bed/left side of the bed) proximity-based positioning problem. Dynamic fingerprints were collected from nine different subjects performing 30 common nursing activities. Extremely randomized trees algorithm achieved the best accuracies of 81% and 71% in the binary and multiclass classifications, respectively. The proposed method can be further used as a proxy for caregiving activity recognition to improve the risk of infection transmission in healthcare settings.

Monitoring fluid intake by commercially available smart water bottles.

Fluid intake is important to prevent dehydration and reduce recurrent kidney stones. There has been a trend in recent years to develop tools to monitor fluid intake using "smart" products such as smart bottles. Several commercial smart bottles are available, mainly targeting health-conscious adults. To the best of our knowledge, these bottles have not been validated in the literature. This study compares four commercially available smart bottles in terms of both performance and functionality. These bottles are the H2OPal, HidrateSpark Steel, HidrateSpark 3, and Thermos Smart Lid. One hundred intake events for each bottle were recorded and analyzed versus ground truth obtained from a high-resolution weight scale. The H2OPal had the lowest Mean Percent Error (MPE) and was able to balance out errors throughout multiple sips. The HidrateSpark 3 provided the most consistent and reliable results, with the lowest per sip error. The MPE values for HidrateSpark bottles were further improved using linear regression, as they had more consistent individual error values. The Thermos Smart Lid provides the lowest accuracy, as the sensors do not extend through the entire bottle, leading to many missed recordings.