Toward Unobtrusive Patient Handling Activity Recognition for Injury Reduction Among At-Risk Caregivers

Smart wearable insoles in industrial environments: A systematic review

BACKGROUND AND PURPOSE

Industrial environments present unique challenges in ensuring worker safety and optimizing productivity. The emergence of smart wearable technologies such as smart insoles has provided new opportunities to address these challenges through accurate unobtrusive monitoring and analysis of workers' activities and physical parameters. This systematic review aims to analyze the utilization of smart wearable insoles in industrial environments, focusing on their applications, employed analysis methods, and potential future directions.

METHODS

A comprehensive review was conducted, involving the analysis of 27 papers that utilized smart wearable insoles in industrial settings. The reviewed articles were evaluated to determine the trends in application and methodology, explore the implementation of smart insoles across different industries, and identify the prevalent machine learning models and analyzed activities in the relevant literature.

RESULTS

The majority of the reviewed articles (67%) primarily focused on human activity recognition and gesture estimation using smart wearable insoles, aiming to enhance safety and productivity in industrial settings. Furthermore, 10% of the studies focused on fatigue identification, 10% on slip, trip, and fall hazard detection, and 13% on biomechanical analyses of workers' body joint loads. The construction industry accounted for approximately 60% of the studies conducted in industrial settings using smart insoles. The most prevalent machine learning models utilized in these studies were neural networks (48%), support vector machines (33%), k-nearest neighbors (30%), decision trees (26%), and random forests (15%). These models achieved median accuracies of 95%, 96%, 91%, 92%, and 95%, respectively. Among the analyzed activities, walking, bending with/without lifting/lowering a load, and carrying a load were the most frequently considered, with frequencies of 10, 10, and 7 out of the 27 studies, respectively.

CONCLUSION

The findings of this systematic review demonstrate the growing interest in implementing smart wearable insoles in industrial environments to enhance safety and productivity. However, the effectiveness of these systems is dependent on factors such as accuracy, reliability, and generalizability of the models. The review highlights the need for further research to address these challenges and to explore the potential of these systems for use in other industrial applications such as manufacturing. Overall, this systematic review provides valuable insights for researchers, practitioners, and policymakers in the field of occupational health and safety.

Clinical human activity recognition based on a wearable patch of combined tri-axial ACC and ECG sensors

Background

In digital medicine, human activity recognition (HAR) can be used to track and assess a patient's progress throughout rehabilitation, enhancing the quality of life for the elderly and the disabled.

Methods

A patch-type flexible sensor that integrated dynamic electrocardiogram (ECG) and acceleration signal (ACC) was used to record the signals of the various behavioral activities of 20 healthy volunteers and 25 patients with pneumoconiosis. Seven HAR tasks were then carried out on the data using four different deep learning methods (CNN, LSTM, CNN-LSTM and GRU).

Results

When ECG and ACC were obtained simultaneously, the overall accuracy rates of HAR for healthy group were 0.9371, 0.8829, 0.9843 and 0.9486 by the CNN, LSTM, CNN-LSTM and GRU models, respectively. In contrast, the overall accuracy rates of HAR for the pneumoconiosis patients' group were 0.8850, 0.7975, 0.9425 and 0.8525 by the four corresponding models. The accuracy of HAR for both groups using all four models is higher than when only ACC signal is detected.

Conclusion

The addition of the ECG signal significantly improves HAR outcomes in the group of healthy individuals, while having relatively less enhancing effects on the group of patients with pneumoconiosis. When ECG and ACC signals were combined, the increase in HAR accuracy was notable compared to cases where no ECG data was provided. These results suggest that the combination of ACC and ECG data can represent a novel method for the clinical application of HAR.

Maximum power point tracking for a multi-layered piezoelectric heel charger with a levered mechanism toward impact-based energy harvesting

The piezoelectric footstep energy harvester does not always work at its maximum power point when the external load is fixed, as the optimal load changes when the walking excitation alters. Thus, the harvesting efficiency is downgraded largely in real-life scenarios compared to in-lab experiments and theoretical or numerical predictions due to the mismatch between the actual load and the optimal load. To address this issue, the concept of Maximum Power Point Tracking (MPPT) is investigated in this paper and the circuit design is implemented for a multi-layered levered piezoelectric footstep energy harvester (heel charger). The proposed event-driven MPPT circuit interface with a customized buck converter aims to maximize the power gained from daily walking using the heel charger to power a fixed load, such as smart insole or shoes. The MPPT circuit design is conceptually simulated and then tested with the heel charger to further validate if it works at its maximum power point when the frequency of the input excitation alters. Results show that the extracted power from the heel charger connected to a fixed resistance load with MPPT implementation is improved up to 300% compared to the one without MPPT implementation in simulation and up to 180% in the experiment when connected to a fixed load. The difference between simulation and experimental results is due to the optimization of using voltage sources as the heel charger and the control signals (pulse width modulation) from the microcontroller in the simulation.

The role of machine learning in the primary prevention of work-related musculoskeletal disorders: A scoping review

To determine the applications of machine learning (ML) techniques used for the primary prevention of work-related musculoskeletal disorders (WMSDs), a scoping review was conducted using seven literature databases. Of the 4,639 initial results, 130 primary research studies were deemed relevant for inclusion. Studies were reviewed and classified as a contribution to one of six steps within the primary WMSD prevention research framework by van der Beek et al. (2017). ML techniques provided the greatest contributions to the development of interventions (48 studies), followed by risk factor identification (33 studies), underlying mechanisms (29 studies), incidence of WMSDs (14 studies), evaluation of interventions (6 studies), and implementation of effective interventions (0 studies). Nearly a quarter (23.8%) of all included studies were published in 2020. These findings provide insight into the breadth of ML techniques used for primary WMSD prevention and can help identify areas for future research and development.

Foot Position Measurement during Assistive Motion for Sit-to-Stand Using a Single Inertial Sensor and Shoe-Type Force Sensors

Assistive motion for sit-to-stand causes lower back pain (LBP) among caregivers. Considering previous studies that showed that foot position adjustment could reduce lumbar load during assistive motion for sit-to-stand, quantitative monitoring of and instructions on foot position could contribute toward reducing LBP among caregivers. The present study proposes and evaluates a new method for the quantitative measurement of foot position during assistive motion for sit-to-stand using a few wearable sensors that are not limited to the measurement area. The proposed method measures quantitative foot position (anteroposterior and mediolateral distance between both feet) through a machine learning technique using features obtained from only a single inertial sensor on the trunk and shoe-type force sensors. During the experiment, the accuracy of the proposed method was investigated by comparing the obtained values with those from an optical motion capture system. The results showed that the proposed method produced only minor errors (less than 6.5% of body height) when measuring foot position during assistive motion for sit-to-stand. Furthermore, Bland–Altman plots suggested no fixed errors between the proposed method and the optical motion capture system. These results suggest that the proposed method could be utilized for measuring foot position during assistive motion for sit-to-stand.

Using a smart textile system for classifying occupational manual material handling tasks: evidence from lab-based simulations

Physical monitoring systems represent potentially powerful assessment devices to detect and describe occupational physical activities. A promising technology for such use is smart textile systems (STSs). Our goal in this exploratory study was to assess the feasibility and accuracy of using two STSs to classify several manual material handling (MMH) tasks. Specifically, commercially-available 'smart' socks and a custom 'smart' shirt were used individually and in combination. Eleven participants simulated nine separate MMH tasks while wearing the STSs, and task classification accuracy was quantified subsequently using several common models. The shirt and socks, both individually and in combination, could classify the simulated tasks with greater than 97% accuracy. Thus, using STSs appears to have potential utility for discriminating occupational physical tasks in the work environment. Practitioner summary: A smart textile system could classify diverse MMH tasks with high accuracy. This technology may help in developing future ergonomic exposure assessment systems, with the goal of preventing occupational injuries.

Validity of Instrumented Insoles for Step Counting, Posture and Activity Recognition: A Systematic Review

With the growing interest in daily activity monitoring, several insole designs have been developed to identify postures, detect activities, and count steps. However, the validity of these devices is not clearly established. The aim of this systematic review was to synthesize the available information on the criterion validity of instrumented insoles in detecting postures activities and steps. The literature search through six databases led to 33 articles that met inclusion criteria. These studies evaluated 17 different insole models and involved 290 participants from 16 to 75 years old. Criterion validity was assessed using six statistical indicators. For posture and activity recognition, accuracy varied from 75.0% to 100%, precision from 65.8% to 100%, specificity from 98.1% to 100%, sensitivity from 73.0% to 100%, and identification rate from 66.2% to 100%. For step counting, accuracies were very high (94.8% to 100%). Across studies, different postures and activities were assessed using different criterion validity indicators, leading to heterogeneous results. Instrumented insoles appeared to be highly accurate for steps counting. However, measurement properties were variable for posture and activity recognition. These findings call for a standardized methodology to investigate the measurement properties of such devices.

Design and Evaluation of a Motorized Robotic Bed Mover With Omnidirectional Mobility for Patient Transportation

Patient transportation in hospitals faces many challenges, including the limited manpower, work-related injuries, and low efficiency of current bed pushing methods. This paper presents a new motorized robotic bed mover with omnidirectional mobility to address this problem. This device is composed of an omnidirectional mobility unit, a force sensing-based human-machine interface, and control hardware with batteries and electronics. The proposed bed mover can be attached to the bottom of a manual hospital stretcher, transforming it into a powered omnidirectional bed (OmniBed) that can be used only by one person. The function of the OmniBed is compared with that of a conventional powered bed, which only provides forward assistance with a fifth powered wheel. We perform a pilot study with 14 subjects to evaluate the performance of this OmniBed and benefits for hospital application. The experimental results show that the OmniBed can half the manpower while decreasing back muscle activities, revealing the potential health benefits for older staffs. The OmniBed also shows the promising signs of high precision and handling in small spaces with its one-step "parallel-parking" ability. This device is more ergonomic, more effective, and safer than the conventional powered bed.

Feature Encodings and Poolings for Action and Event Recognition: A Comprehensive Survey

Action and event recognition in multimedia collections is relevant to progress in cross-disciplinary research areas including computer vision, computational optimization, statistical learning, and nonlinear dynamics. Over the past two decades, action and event recognition has evolved from earlier intervening strategies under controlled environments to recent automatic solutions under dynamic environments, resulting in an imperative requirement to effectively organize spatiotemporal deep features. Consequently, resorting to feature encodings and poolings for action and event recognition in complex multimedia collections is an inevitable trend. The purpose of this paper is to offer a comprehensive survey on the most popular feature encoding and pooling approaches in action and event recognition in recent years by summarizing systematically both underlying theoretical principles and original experimental conclusions of those approaches based on an approach-based taxonomy, so as to provide impetus for future relevant studies.

Network Coded Cooperative Communication in a Real-Time Wireless Hospital Sensor Network

The paper presents a network coded cooperative communication (NC-CC) enabled wireless hospital sensor network architecture for monitoring health as well as postural activities of a patient. A wearable device, referred as a smartband is interfaced with pulse rate, body temperature sensors and an accelerometer along with wireless protocol services, such as Bluetooth and Radio-Frequency transceiver and Wi-Fi. The energy efficiency of wearable device is improved by embedding a linear acceleration based transmission duty cycling algorithm (NC-DRDC). The real-time demonstration is carried-out in a hospital environment to evaluate the performance characteristics, such as power spectral density, energy consumption, signal to noise ratio, packet delivery ratio and transmission offset. The resource sharing and energy efficiency features of network coding technique are improved by proposing an algorithm referred as network coding based dynamic retransmit/rebroadcast decision control (LA-TDC). From the experimental results, it is observed that the proposed LA-TDC algorithm reduces network traffic and end-to-end delay by an average of 27.8% and 21.6%, respectively than traditional network coded wireless transmission. The wireless architecture is deployed in a hospital environment and results are then successfully validated.