**1. Introduction**

Falls are one of the leading causes of both fatal and nonfatal injuries in clinical [1], geriatric [1], occupational [2], and healthy athletic populations [3] and can be induced due to environmental factors as well as physical and psychological human factors [4]. Subsequently, fall prevention methods and interventions are carried out by a wide range of healthcare and rehabilitation professionals [1]. With the advent of technology, multiple smart tools and applications have been used to combat falls using fall prevention intervention and specifically for detecting and diagnosing falls and fall risk [1]. Detecting falls or fall risks during everyday activities through activity monitoring can be beneficial during both prefall intervention, for individuals who are at fall risk, and postfall intervention, and for individuals

who have already sustained a fall, in order to reduce their risk of subsequent falls [1]. While fall injury prevention sensor systems focus on responding to a fall after it has occurred and aid in contacting emergency medical assistance, fall detection sensor systems attempt to identify discrete fall events over the course of the day [1] and subsequently detect fall risk.

Wearable sensors have been used for human activity monitoring in various fields such as sports, training, fitness for improving performance, and preventing injuries and additionally, have also been used successfully in monitoring physical activity in clinical, pathological, and aging populations [5]. These wearable devices include different types of sensors such as inertial motion sensors (IMUs) [6,7], accelerometers [8,9], gyroscopes, magnetometers, switches, pedometers, goniometers, and foot pressure sensors that can provide kinematic and kinetic information of the body's movement [4,10–12]. Additionally, these devices have also been used in conjunction with other smart tools such as smart phones, smart shoes, modern camera systems, and even low-cost infrared thermal imaging sensors [13–17]. However, these sensors also have their own limitations, with a critical one being IMU distortion and drift [18,19], which can lead to an inaccurate representation of human activity monitoring. With the fast growth of sensor technology, several challenges towards design, development, fabrication, implementation, and utilization for continuous monitoring exist [9]. Recently, Luczak et al. [20] reported the current status of lack of wearbale solutions to accurately capture data "from the ground up" and the need for "closing the wearbale gap" through development and validation of novel types of sensors. Although this was specific to sensors used for the athletic population [20], there has also been a need for developing and validating novel sensors for fall detection, which is a leading cause for fatal and nonfatal injuries across different populations [1–4]. Hence, development and validation of other forms and types of wearable sensors to monitor human activity with more accuracy and less limitations, specifically for fall detection is required to close the wearable gap.

A hierarchy of approaches for fall detection has been previously proposed that includes camera-based systems to assess change in body shape, inactivity detection or three-dimensional (3D) head motion analysis, an ambience device that determines posture and presence, and wearable devices that evaluate posture and motion [5]. However, the camera-based systems and ambient device systems have their own limitations [6] such as capture obstruction, privacy concerns, false alarms, battery life, and sole intended use of the device [7,8]. Previous literature has reported that wearable devices can successfully detect induced falls in a laboratory setting [21] or other indoor environments [8]. Subsequently, different types of body-worn or wearable sensors appear to be the prominent choice for fall detection [5,10,22]. Early detection of fall risk, near falls, and incidences of falls classified by types (slip or trip induced) using wearable sensor technology can help aid in minimizing fall and fall-related injuries [11,21–23]. Due to higher precision, lower time commitment, easy administration, and feasibility, wearable biomechanical sensors are becoming popular for early detection of falls [8]. A recent review paper by Rucco et al. [22] addressed the impact of wearable sensors in fall detection by reporting the average number/age of participants; number of sensors, type of sensors, and their placement used in such fall detection studies. The predominant sample of populations tested included young and old individuals with age groups of less than 30 years of age and more than 64 years of age, and used a sample size of less than 10, 10–19, and 20–100 more commonly [22]. The most commonly used type of sensor being an accelerometer (more than 70%), followed by pressure sensors and gyroscopes, magnetometers with one or two sensors, predominantly placed and located on the trunk, foot, and leg [22]. More recently, a stretchable soft robotic sensor (SRS) that records a change in resistance values when stretched was used to determine if ankle joint-type movements could be inferred by using a custom-built rigid-body ankle joint mechanical device [20]. Based on the findings from this study, the SRS was capable of providing significant linear models in predicting sagittal plane ankle joint movement specific to plantarflexion [20]. As an extension of this research, a follow-up study by the same researchers successfully used similar stretch sensors that record capacitance change in response to stretch, to identify and detect ankle joint movements of plantarflexion (PF), dorsiflexion (DF), inversion (INV), and eversion (EVR) in human participants

during non-weight-bearing isolated ankle movements [24]. Results from these research studies are published as "Closing the wearable gap: Part I and Part II" [20,24]. However, these stretch or flexible sensors have not ye<sup>t</sup> been utilized to identify ankle joint movements in the more dynamic range, such as during slips and trips for fall detection.

Placement and position of wearable sensors used for quantifying body movements, balance, gait, and overall physical activity vary greatly across di fferent parts of the body, ranging from the upper torso, lower torso, and lower extremities [10]. Specific to fall detection, the most commonly used sensor placement position includes the waist or hip, followed by trunk attachments [10]. Head and neck placements have also been used to assess acceleration patterns of the head during falls [25]. A higher success in detecting falls has been achieved by placing wearable sensors at the center of mass of the body [7]. However, the human body is considered as an inverted pendulum during upright balance maintenance, with the ankle joint serving as the axis of rotation [26]. Hence, placing wearable sensors on the foot and ankle segmen<sup>t</sup> can aid to capture recoveries and falls from a distal-to-proximal direction (ankle strategy) [26]. Previous research has used IMU sensors placed at the left and right ankle and sternum to successfully classify fall types based on slips and trips [27]. However, the use of an SRS sensor placed at the ankle and foot segmen<sup>t</sup> in detecting falls has not been analyzed.

Falls due to slips and trips are induced by a postural perturbation to the human body [28,29]. A postural perturbation is a sudden change in the orientation of the body that causes body disequilibrium and may lead to the displacement of the total body center of mass [26], thereby contributing to falls. One of the primary needs for fall detection is the assessment of postural responses during unexpected and expected postural perturbations, be it the "closed-loop" feedback postural control system when the external perturbations are unexpected and governed by an anticipatory sensory–motor, or "open-loop" feedforward postural control system when the external perturbations are expected [28]. Falls in the backward and forward directions are commonly studied in both real-world falls and simulated falls [10]. Simulated falls in a closed and controlled environment have been commonly used to analyze falls using fall prevention harness systems to protect the participants from any undesired falls. A systematic review on fall detection with body-worn sensors reported that 90 di fferent studies (93.8% of the studies) used simulated falls [10]. Biomechanical analyses of human movement have evolved from simple goniometric measures to technologically advanced optical three-dimensional (3D) motion capture systems, with the latter seen as the gold-standard measure. However, the combination of SRS with ankle–foot placements during di fferent postural perturbations in detecting falls, validated against a 3D motion capture system, has not been examined. Therefore, the purpose of the study was to validate the use of a stretchable SRS against a 3D motion capture system to identify ankle joint kinematics during both unexpected and expected slip and trip perturbations for fall detection. It was hypothesized that the SRS would be a valid tool for detecting ankle joint movements during both unexpected and expected postural slip and trip perturbations.

### **2. Materials and Methods**
