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International Journal of Environmental Research and Public Health
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  • Open Access

19 May 2020

Wearable Stretch Sensors for Human Movement Monitoring and Fall Detection in Ergonomics

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1
Neuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA
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Department of Human Factors & Athlete Engineering, Center for Advanced Vehicular Systems (CAVS), Mississippi State University, Mississippi State, MS 39762, USA
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Department of Industrial & Systems Engineering, Mississippi State University, Mississippi State, MS 39762, USA
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Department of Electrical & Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA
Int. J. Environ. Res. Public Health2020, 17(10), 3554;https://doi.org/10.3390/ijerph17103554
This article belongs to the Special Issue Injury Prevention and Safety Promotion: A Physical and Cognitive Ergonomic Approach
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Abstract

Wearable sensors are beneficial for continuous health monitoring, movement analysis, rehabilitation, evaluation of human performance, and for fall detection. Wearable stretch sensors are increasingly being used for human movement monitoring. Additionally, falls are one of the leading causes of both fatal and nonfatal injuries in the workplace. The use of wearable technology in the workplace could be a successful solution for human movement monitoring and fall detection, especially for high fall-risk occupations. This paper provides an in-depth review of different wearable stretch sensors and summarizes the need for wearable technology in the field of ergonomics and the current wearable devices used for fall detection. Additionally, the paper proposes the use of soft-robotic-stretch (SRS) sensors for human movement monitoring and fall detection. This paper also recapitulates the findings of a series of five published manuscripts from ongoing research that are published as Parts I to V of “Closing the Wearable Gap” journal articles that discuss the design and development of a foot and ankle wearable device using SRS sensors that can be used for fall detection. The use of SRS sensors in fall detection, its current limitations, and challenges for adoption in human factors and ergonomics are also discussed.

1. Introduction

Wearables are often defined as “technologies used to measure various physiological and kinematic parameters by being sported or borne by the user” [,]. The purpose of wearable technology or devices is to assess human performance—that is, biomechanical or physiological in nature—or for monitoring specific events of human movement in daily living, athletic, clinical, or occupational populations. The advantage of wearable devices is that they allow for monitoring human performance continuously and in environments that are outside of a laboratory or clinic with ease. This advantage can help to assess, diagnose, treat, and prevent injuries, especially in occupational settings where there is an elevated risk for work-related injuries. According to the National Safety Council (NCS), in 2017, a total of 4.5 million work-related medically consulted injuries and 4414 preventable work-related deaths occurred in the United States []. Falls are the leading cause of both fatal and nonfatal injuries in occupational populations []. Falls and fall-related injuries can be attributed to postural instability caused by an induced loss of balance and failure to recover from the imbalance, which commonly occurs in hazardous occupations. The Bureau of Labor Statistics (BLS) reported that, in 2017, from a total of 5147 fatalities, 887 were attributed to falls, slips, and trips, and a total of 227,760 cases of nonfatal workplace injuries were due to falls (47,180 falls to a lower level, 142,770 same-level falls, and 33,720 slips/trips), with a high incidence rate especially in construction (24,160 falls) and manufacturing (22,010 falls) []. Moreover, the innate dangers in hazardous occupations such as construction, manufacturing, transportation, warehousing, mining, quarrying, and healthcare services, as well as emergency responders—such as firefighters, law enforcement, and military—predisposes greater risks for occupational injuries [,,,,,]. In addition to the hazardous work conditions, physical exertion mandated by the occupational tasks creates greater demands on the human postural control system, thereby increasing the risks of falls []. Furthermore, the economic and financial costs associated with work-related accidents and injuries pose a significant threat and burden to the nation and the world. In 2017, the NCS reported $161.5 billion as an estimated cost for work-related injuries in the United States []. The constant increase in injury, illness, and accident rates in the workplace warrants the successful implementation of safety practices that are evidence-based. This further warrants the need for new innovating and emerging research to minimize workplace fall-related accidents.
With greater advancements in technologies, there are multiple tools and equipment, such as camera-based systems, ambient sensors, and various types of wearable sensors, that are helpful to detect falls and near falls in an attempt to reduce fall-related injuries []. In this context, monitoring employees through wearable sensors for potential falls or near-falls during occupational activities will aid not only in detecting falls but can also help in pre-fall and post-fall interventions []. The traditional fall prevention technologies such as the camera-based systems, ambient systems, and fall alert sensor systems identify falls after they have occurred and help to contact emergency services. Whereas, wearable technologies are used as fall monitoring and detection systems that help to identify discrete fall or near-fall events over the course of the day [] and can be extremely beneficial, especially during high fall-risk occupational tasks. With the ever-increasing fall risk in hazardous occupations, there is a need to mitigate such injuries and improve safety.
Although there are multiple sensors being used for human monitoring, the advent of wearable stretch/strain sensors (WSS) that are either worn or attached to the skin is more recent. Hence, this paper provides an in-depth review of the current WSS technology for human movement monitoring by addressing their uses, applications, findings, limitations, and future scope. This paper also recapitulates the findings of a series of five published manuscripts from ongoing research that are published as Parts I to V of “Closing the Wearable Gap” journal articles [,,,,] that discuss the design and development of a foot and ankle wearable device using wearable soft robotic stretch (SRS) sensors that can be used for fall detection. The use of SRS sensors in fall detection, its current limitations, and challenges for adoption in human factors and ergonomics are also discussed.

2. Wearable Technology

The wearable technologies used to capture metrics about human performance often receive most of the focus. Performance assessment wearables are largely responsible for the booming growth in wearable user consumption, which began at the 2014 Consumer Electronics Show and is expected to hit $34 billion [] to $40 billion [], with an estimated 485 million devices shipped, in 2019. The purpose of most wearables across many environments is to paint a complete picture of what continuous work outside of the lab does to the human “athlete”—be they sports athletes, industrial athletes, tactical athletes (war fighters and first responders), or even the at-risk athletes who are in rehabilitation or longer-term treatment. The mass popularization of smartphone and mobile device technology has enabled the miniaturization of data-capturing sensors and other processing and storage components, such that computers can be embedded into clothing and other noninvasive locations on a person while they are actively performing a task. How individuals work and, in turn, how that work affects them can be more effectively optimized, quantified, and tracked. More advanced wearable electronic sensors exist that range in their applications from detecting biomechanical movements [,], haptic and touch perception [,,], human physiological responses [], and even bioinspired sensors that mimic the functions of the human sensory nervous system []. These advanced wearable electronic sensors were developed and validated predominantly for bridging the gap in the human-machine/computer interface literature and their applications [,]. These sensors aid in capturing precise human responses and aid multiple aspects of applications ranging from clinical, rehabilitation, athletic, and occupational populations.

3. Wearable Stretch Sensors

The WSS have numerous applications that involve motion capture studies. For body strain measurements, these can be integrated onto clothing or directly laminated on human skin. Measurements ranging from minute skin motions induced by respiration and heartbeat to more significant human body strains like the bending or straightening of body joints can be obtained [,]. The information obtained from these sensors can be used to evaluate body movements, posture, and performance of the player during sports activities [,]. The information recorded could be useful for monitoring the body performance and wellness analysis of an individual. Another application of SRS involves mounting them on the knee joint [,,]. This helps in gaining information about different knee patterns, such as walking, running, jumping, squatting, and various other activities. WSS are beneficial for continuous health monitoring, rehabilitation, and the evaluation of human performance.

3.1. Review of Wearable Stretch Sensors for Human Movement Monitoring

A brief review of a variety of such WSS and skin-mounted sensors with their broad applications in human motion detection have been summarized in Table 1. The review table surveys various studies conducted on the application of WSS that include both resistive and capacitive types of sensors. The table highlights the potential application of these wearable sensors as motion-capturing devices and comprises of a review of human movement monitoring, gait analysis, and other movement-based applications using such WSS. Table 1 also includes information about the current challenges and limitations in the use of skin mountable and wearable sensors for body-integrated applications [,,,,,,,,,,,,,].
Table 1. A review of different studies assessing human movement with the use of wearable stretch sensors with descriptions of the study applications, tests conducted, and findings, as well as limitations and future scope.

3.2. Design and Development of Wearable Devices Using Soft Robotic Stretch (SRS) Sensors for Human Movement Monitoring

While several wearable devices that incorporate different types of sensor technology exist for fall detection, they have their own limitations, such as inertial measurement unit (IMU) distortion, reliability, and high financial costs [,]. Moreover, there is a constant need for the design and development of novel wearable technology to combat the increasing threat of falls and fall-related injuries in occupational settings. Our research team was tasked with the design and development of a wearable device using soft-robotic stretch (SRS) sensors capable of capturing the human joint movement kinematics, specifically at the ankle joint in the lower extremity. The research team has since then published a series of five papers under the “Closing the Wearable Gap” series: Part I to Part V [,,,,], which discuss the design, development, and testing of the foot and ankle wearable device. Specifically, the Parts I and II papers tested the reliability and feasibility of using SRS on both a mechanical ankle joint device and on human participants [,]. The SRS are thin strap-like electronic sensors that produce a linear change in voltage recorded either in resistance (LiquidWire, Beaverton, Oregon, USA) or capacitance (StretchSense, Auckland, NZ, USA) when they are stretched (Figure 1). Subsequently, when the SRS were fixed on the anterior, posterior, medial, and lateral sides of the foot and ankle segments spanning across the ankle joint axis, they stretch during all four degrees of freedom of the ankle joint, plantar flexion, dorsiflexion, eversion, and inversion movements, respectively. The change in voltage was correlated to the change in the ankle joint range of motion angles using traditional electric goniometers as well as using the gold standard 3D motion capture system. The results from these papers identified significant linear models and validated with significant goodness-of-fit when compared to the gold standard 3D motion capture system [,]. The linearity of the stretch from the SRS was reported to have an R2 value of 0.99 in the Part I paper and an R2 value of 0.95–0.99 in the Part II paper. Thus, the Parts I and II papers (Figure 2) demonstrated that the SRS sensors could be used as a potential wearable device to detect ankle joint kinematics in both sagittal and frontal movements of plantar flexion/dorsiflexion and inversion/eversion movements, respectively. However, the movements in these two studies were performed one at a time from a static, non-weight-bearing condition. The need for assessing the use of SRS sensors in dynamic movements, especially fall detection, was necessary. The critical advancement of studies exploring more complex movements lead to the Parts III, IV, and V papers (Figure 2) [,,], the next projects investigated by the research team, which are explained further with in-context of WSS in fall detection, their applications, limitations, and future scope.
Figure 1. Different wearable stretch sensors used by the current research team. From left to right: (1) StretchSense Pressure Sensor, (2) StretchSense Displacement Sensor, (3) LiquidWire Flocked Sensor, (4) LiquidWire Silicone Sensor, and (5) LiquidWire Elastic Sensor.
Figure 2. A pictorial representation of past and future works from the “Closing the Wearable Gap” (CWG) journal article series (Parts I to V). (1) CWG Part I—Mobile Systems for Kinematic Signal Monitoring of the Foot and Ankle, (2) CWG Part II—Sensor Orientation and Placement for Foot and Ankle Joint Kinematic Measurements, (3) CWG Part III—Use of Stretch Sensors in Detecting Ankle Joint Kinematics during Unexpected & Expected, Slip & Trip Perturbations, (4) CWG Part IV—3D Motion Capture Cameras Versus Soft Robotic Sensors Comparison of Gait Movement Assessment, (5) CWG Part V—Development of Pressure-Sensitive Sock Utilizing Soft Sensors, and (6) CWG—future iteration of an ankle sock concept to monitor human movement.
Although different types of sensors are being used for fall monitoring and detection, the placement of these sensors on the human body have been limited predominantly to the torso and lower extremities [], and body-worn sensors used for fall detection have also been traditionally placed on the waist/hip or as trunk attachments []. Occasionally, wearable sensors such as accelerometers that are placed on the head and neck have also been utilized that detect the acceleration changes of the head in the event of falls []. However, based on the postural stability model suggested by Winter (1995), the human body is considered as an inverted pendulum, with the axis of rotation pivoted at the ankle joint []. Subsequently, placing the SRS sensors across the ankle joint axis allows the researchers to monitor the kinematics of the ankle joint complex from an inverted pendulum model aspect. The SRS sensors placed on the anterior aspect of the feet stretches during plantar flexion, while the one placed on the posterior aspect of the feet stretches during dorsiflexion. Similarly, the SRS sensor on the lateral aspect of the ankle stretches during inversion, and the one on the medial aspect stretches during eversion. Correlating the linear change in voltage due to the stretch of the SRS with changes in the ankle joint range of motion in degrees quantified using 3D motion capture enables constant monitoring of ankle joint kinematics during human physical activity.

4. Wearable Sensors and Fall Prevention

4.1. Current Wearable Technology in Fall Monitoring and Detection

While camera-based and ambient systems have been used for fall detection based on changes in body movement and posture, movement inactivity detection, and head motion analysis, these solutions have their limitations, such as the obstruction of capture volume, privacy, false alarms, and battery life [,]. Wearable devices have been successfully implemented and used to assess human physical activity in multiple populations []. More specifically, wearable or body-worn sensors have become the preferred choice of technology for fall monitoring and detection [,,] due to their high precision, less time commitment, easy access, feasibility, and administration []. These wearable devices include inertial measurement units (IMUs), accelerometers, gyroscopes, magnetometers, pedometers, electric goniometers, and foot pressure sensors [,,,,,]. More often than not, these physical, wearable sensors have been used along with smartphones and applications to provide an effective wearable fall-detection device and system [,,,,].

4.2. Use of SRS Sensors for Fall Detection

With the SRS sensor design completed, the Parts III and IV papers address the validation of the SRS sensors during dynamic tasks specific to slip and trip perturbations [] and while walking on sloped surfaces [] (Figure 2). In Part III of the series, participants wore SRS sensors and were subjected to both unexpected and expected postural perturbations imparted by the sudden starting and stopping of a treadmill belt from the static nonmoving position. All sensor data were compared to ankle joint plantar flexion/dorsiflexion quantified using a 3D motion capture system. Trials during which the treadmill belt moved forward in relation to the individual were used to create slip perturbations, and the trials during which the treadmill belt moved backward in relation to the individual were used to create trip perturbations. The unexpected trials were created when the participants were not informed of the upcoming perturbation type and time and were provided randomly within 30 s of static stance. The expected trials were when participants were informed of the upcoming perturbation type and time and were counted down numerically to provide the perturbation. The use of both slip and trip perturbations and both unexpected and expected perturbations were in an attempt to assess the validation of the SRS sensors during different types of fall detection and for the validation of the behavior of the SRS sensors during rapid unexpected and braced expected falls. Adjusted R2 and root mean square error (RMSE) were used to validate the SRS sensor data with the 3D motion capture ankle angle kinematics. The results from the study identified a medium-to-high adjusted R2 value (R2 = 0.60) and a low RMSE value (<4 degrees), thus suggesting a moderate-to-high accuracy with minimal errors in comparing the SRS sensors against the 3D motion capture system during these different postural perturbations. For verification, R2 and RMSE have shown to be valuable assessment methods for kinematic and kinetic data related to sports and dynamic movements []. Thus, the findings suggest that the SRS sensors could be a feasible option in detecting ankle joint kinematics during slip and trip-induced falls [].
As a follow-up to the slip-trip testing, the Part IV paper addressed the validity of SRS sensors during walking both on a flat surface and a tilted surface []. All four SRS sensors for capturing plantar flexion/dorsiflexion and inversion/eversion were used to measure ankle joint kinematics simultaneously. Participants walked with a self-regulated pace on a custom-built wooden platform, and a total of 12 gait trials, with six on each surface (flat and titled), were collected to acquire a total of 24 gait cycles for each participant. In addition to the previously used adjusted R2 and RMSE, the mean absolute error (MAE) was calculated to validate the SRS sensor data with the 3D motion capture ankle angle kinematics for all four degrees of freedom. The findings indicated that all four SRS sensors provided a successful fit identified by a high adjusted R2 value (R2 = 0.854) and lower MAE (MAE = 1.54) and RMSE values (RMSE = 1.96), suggesting that SRS sensors could be a feasible option to capture ankle joint kinematics both on flat, as well on tilted, surfaces []. The validity of the SRS sensors during dynamic walking on tilted surfaces from Part IV [] and during rapid slip-trip perturbations from Part III [] suggest that SRS sensors could be a new wearable device that can detect ankle joint kinematics in fall-prone conditions, where the human body is subjected to different perturbations that destabilize the body postural stability (Figure 2).
Finally, a comprehensive fall detection system could not just rely on capturing joint kinematics, and capturing kinetics, especially forces from the feet during ground contact, need to be prioritized as well. Subsequently, Part V of the paper series [] successfully attempted to develop a pressure-sensitive sock using a compressible variation of the same SRS sensors (five sensors) placed on the sole of each foot enclosed in a sock. Pressure (kPa) from the soles of the feet were quantified using pressure cells (BodiTrak™ Vista Medical, Winnipeg, MB, Canada), as well as with ground reaction forces (N) from dual-force platforms (Kistler™ Novi, MI, USA), compared to the compressible SRS sensors during different activities such as squatting, shifting weight from left to right, and shifting weight from heels to toes. Correlations—mean R2 and mean RMSE—were used to compare the changes in pressure of compressible SRSs, changes in pressure on the BodiTrak™ Vector Plate, and changes in force on the Kistler™ Force Plates. The results identified a positive linear relationship between the compressible SRS sensors and BodiTrak™, while the comparison to the force plates was inconclusive []. Based on the findings, the compressible SRS sensors were still identified as an effective option to capture the pressure distribution from the sole of the foot during ground contact, which serves a vital purpose in identifying the weight-bearing status of the specific lower extremity in the event of falls or near-falls. In the context of a fall detection system using the SRS sensors, the need for kinetic data from the feet during ground contact, in addition to the ankle joint kinematics, is further explained under the current limitations sections.

5. Limitations and Future Scope

5.1. Limitations to Wearable Stretch Sensors

There are several limitations and future developments to be considered when working with SRS. Factors such as placement of the sensors, perception of space, body diversity in anthropometry and movements, attachments, containments, sensory interactions, aesthetics, and long-term use play very vital roles on the wearability of the sensors. The wearable SRS must be durable enough to perform consistently for the time and conditions of use in which they are expected to collect data. Some of the challenges faced when using SRS include sliding and distortion of the sensors during skin deformations, impact with an object and stress-induced movements, leading to underestimation of the actual motion [,,,]. Exploiting textile engineering techniques, collaboration between designers and engineers would help to improve smart clothing designs from a noninvasive and comfort perspective. Manufacturing clothes with close-fitting garments would help to minimize sensor movements and drift, thereby improving accuracy.
Another possible source of error for an SRS-based device was nonlinearity of the wearable sensors under compressive force. Hysteresis, which can be defined as a natural reluctance for the sensors to return to the original length after removal of a load, and the nonlinearity of sensors due to such changes in its material properties further add to the complexity and difficulties with sensor errors []. The error concerning hysteresis and nonlinearity was observed as a major drawback for all resistive-type wearable sensors. Efforts have been made to add a pressure-sensing element on top of the strain-sensing element, allowing the sensor to detect compression in addition to the strain []. Under dynamic loading conditions, hysteresis was observed in the sensor response, which can be due to the ways sensors were integrated to the body or might have been caused due to the viscoelastic nature of the polymers. Carefully selecting the location of the sensors—that is, moving their location from directly on top of the joints to more soft and flat areas of the body—would help reduce the pressure effect as well; however, the changes concerning relocation of the sensors would make the design process more complicated. Recently, deep-learning methods have been proposed for full-body motion sensing to solve the problems of nonlinearity and hysteresis [].
The accuracy of the sensor readings highly depends on the care taken during the calibrations. Even a slight difference in the calibration leads to drastic changes in the joint angle prediction [,,]. There were differences noted in the actual and predicted angles. Various approaches have been proposed using different kinds of devices, such as an electro-goniometer, IMUs, and camera-based optical systems. However, these solutions all still had some limitations, such as inaccuracy in detecting multiple degrees of freedom for joint motions, errors with high-speed motions, and space confines [,,]. Additionally, discomfort of the wearers due to the attachment of rigid electronics on garments or skin were reported. Based on studies concerning calibration datasets during motion sensing, a robust calibration process for motion capture using computational methods involving machine-learning and deep neural network systems is required to deal with the issues more effectively [,].

5.2. Current Limitations of the Stretchable SRS and Measures to Minimize Errors in Fall Detection

Even though the SRS sensors were validated against a motion capture system and identified as a potential fall detection sensor, both during unexpected and expected slip and trip perturbations [], as well as during walking on sloped surfaces [], a few limitations still exist. The data from just the four SRS sensors on the foot and ankle segment used to identify ankle joint kinematics [,] would not essentially be a comprehensive fall detection system. Deviations from acceptable changes in ankle joint kinematics are used to detect any aberrant movements during the course of a physical task. For example, during a slip-induced fall, a change of 30 degrees in plantar flexion could be evident. However, while going up on one’s toes during a reaching to a height task, a similar change of 30 degrees in plantar flexion is possible. Hence, to differentiate a fall-induced angular displacement from a task-induced one, the rate of change of angular displacement, angular velocity, should also be quantified, as slip or trip-induced falls tend to have a faster angular velocity. Yet, assessing kinematics of the ankle joint alone may still not be sufficient to have a precise fall detection system. The addition of SRS sensors repurposed as a pressure-sensitive sock to detect pressure underneath the sole of the foot will aid in the identification of the weight-bearing status of the lower extremities []. Subsequently, the knowledge of the pressure distribution during weight-bearing activities and the absence of pressure during non-weight-bearing conditions can aid in identifying the context in which the ankle joint is moving. Similarly, during weight-bearing activities such as walking, the presence of different ratios of pressure distribution can aid in detecting the different subphases of the stance phase of the gait cycle. As such, the presence of pressure distribution in one foot but the absence in the other with periodic repetitions can indicate the stance phase and swing phases of the gait cycle during walking. However, the absence of pressure distribution in both feet accompanied by extremes of the joint range of motion kinematics can potentially indicate a fall event. The current limitations of the SRS sensors can be minimized with the above-discussed measures, as well as with the addition of different types of sensors, as discussed in the below section on future SRS sensor development. However, repeated testing both in the laboratory and in the field, especially in hazardous occupational environments, such as in the roofing and construction industry, is essential.

5.3. Future Stretchable SRS Development for Fall Detection

The future of SRS sensors being incorporated into fall detection devices, especially in addition with other types of sensors working in unison, can provide high-quality data of human movement and the utmost precision in detecting and preventing falls and alarms in fall detection. The existing, wearable system of four SRS sensors on the ankle joint axis to measure the ankle joint kinematics and five sensors on the sole of the foot to measure foot pressure can still be enhanced by adding other sensors to make a comprehensive, wearable fall detection system. For example, IMUs and accelerometers have been previously used to detect abnormal movement patterns of the body [,] and electromyography (EMG) recordings, especially from the lower extremity muscles, to detect pre-falls to the ground in the forward, backward, and lateral directions []. While these wearable devices have been used in isolation, the impact of a comprehensive, wearable fall detection system utilizing different sensors is still lacking. Finally, the use of SRS sensors allows them to be sewn into compression garments as they contour around the shape of the body, potentially paving the way for future smart garments of fall detection in the workplace.

6. Challenges to the Use of Wearable Devices in the Workplace

Wearable technologies are being increasingly promoted and used in the workplace for employee safety and injury prevention []. Specific to fall prevention in the workplace, even though personnel protective equipment (PPE) such as fall-harnesses and fall-arrest systems are mandated for fall-risk workplaces, the use of wearable technology provides an opportunity to continuously monitor the safety status of the employees and to find at-risk employees who are more prone to fall. Individuals who might have their postural control system compromised due to any neurological or musculoskeletal disorders, due to the hazardous working conditions (such as working in fall-risk conditions, working in awkward postures, and improper or poor PPE availability and use) or due to the inherent hazards of the occupation (such as physical and mental fatigue, overexertion, etc.) can be identified early before events of falls and given appropriate training and safety precautions. While the use of wearable technology seems to aid the well-being of the employee and minimize the financial cost to the organizations due to fall accidents, there are still challenges to their adoption. A recent study by Schall et al. (2018) identified barriers such as employee privacy, compliance, wearable device’s durability, and the cost-benefit ratio, which have prevented the widespread adoption of wearable technology in the workplace []. Even though the study by Schall et al. (2018) did not focus on specific types of wearable devices pertaining to fall detection, the perception of the identified barriers and, in turn, the adoption remains a challenge in the workplace. Subsequently, the incorporation of multiple types of sensors specific to the occupational tasks can provide a comprehensive employee monitoring system to prevent injuries and promote safety. Reducing the injury risk and increasing employee satisfaction, wellness, and productivity have been identified as potential benefits of using wearable devices in the workplace []. Meanwhile, organizations that intend to adopt wearable technology need to focus on workplace safety and inform and support the employees of wearable technology and address the barriers for adoption [,]. The implementation of SRS sensors for fall monitoring and detection will also face the same barriers as other wearable technologies. Multiple field-testing and awareness creations of the scientific community will aid in breaking the barriers for adoption and increase the use of wearable SRS fall detection systems [,].

7. Conclusions

This paper provides a review of the current WSS, a summary of the current research team’s efforts to design, develop, and test a foot and ankle wearable device with the use of a novel SRS sensor and, subsequently, propose this wearable device as a potential fall detection system in the field of human factors and ergonomics, while addressing the limitations, future scope, and challenges of such wearable devices in the workplace. The SRS sensor has been validated in five different studies published as a series of Parts I to V papers in the “Closing the Wearable Gap” research. Over the course of the design and development of this wearable device, foot and ankle joint kinematics and kinetics captured by the SRS sensors were validated against an electronic goniometer, 3D motion capture systems, pressure mats, and force platforms. Specific to fall detection, the foot and ankle wearable device using the SRS sensors was identified as a promising technology to detect falls by assessing ankle joint kinematics during unexpected and expected slips, trips, and walking on tilted walkways. Thus, based on the current available literature, their findings, limitations, and future scope, this paper attempts to “Close the Wearable Gap” on WSS and their use in human movement monitoring and fall detection.

Author Contributions

Conceptualization, H.C. and R.F.B.; software, J.E.B., D.S., W.C., and T.L.; validation, D.S., W.C., and A.T.; resources, P.T. and S.N.K.K.A.; writing—original draft preparation, H.C.; writing—review and editing, H.C., P.T., R.F.B., S.N.K.K.A., A.K., B.K.S., T.L., and R.K.P.; and funding acquisition, R.F.B. All authors have read and agreed to the published version of the manuscript.

Funding

The Parts I to V of the Closing the Wearable Gap papers were funded by the National Science Foundation under NSF 18-511—Partnerships for Innovation award number 1827652.

Acknowledgments

The authors wish to thank all the graduate and undergraduate students who aided in the completion of the Parts I to V of the Closing the Wearable Gap papers.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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