Musculoskeletal diseases, such as osteoarthritis and tendinopathy, impose substantial burden on individuals and health care systems. As a community of scientists and clinicians, we have been largely ineffective in managing musculoskeletal diseases, as current prevalence, incidence, and socioeconomic burden are at alarming levels and projected to increase sharply in coming decades (Hunter et al., 2014). In particular, we have a limited understanding of how physical behavior, i.e., whole-body mechanics, influences tissue state (Forwood and Burr, 1993), and this could underpin our failure to cure, or curb, these prevalent, harmful, and costly diseases. A case in point is the study of the effects of physical activity on cartilage morphology. Studies of animals (Kiviranta et al., 1987, 1988, 1992; Newton et al., 1997) and humans (Jones et al., 2000, 2003; Roos and Dahlberg, 2005) have reported increased physical activity to be associated with positive structural and biochemical adaptations in weight-bearing joints, while other studies have reported no effects of physical activity on bulk measures of cartilage morphology (Eckstein et al., 2002, 2006).

The failure to effectively treat musculoskeletal disease is frustrating for scientists and clinicians alike. We possess a wealth epidemiologic data detailing risk factors for many musculoskeletal diseases, e.g., increased age, female sex, body mass, prior joint trauma, obesity, abnormal physical activity levels, and joint structural deformity (Felson et al., 1997, 2000, 2013; Cooper et al., 2000; Coggon et al., 2001; Lohmander et al., 2004, 2007; Roemer et al., 2009; Andriacchi et al., 2015). At tissue- and sub-tissue levels, studies have explored the effect of loading on structure and biology (Radin and Paul, 1971; Simon et al., 1972; Radin et al., 1973, 1984; Rubin and Lanyon, 1985; Forwood and Turner, 1995; Wang et al., 2013, 2015; Joo Kim et al., 2016). However, integrating experimental results with whole-body-, tissue-, and cell-level computational models, and using these models to modulate physical behavior to affect musculoskeletal tissue health remains challenging (Erdemir et al., 2015). In a recent narrative review, Ng et al. (2017) proposed physical therapy to enhance and promote tissue regeneration, linking external mechanical stimuli to tissue mechanobiology. In line with Ng et al. (2017), we describe an approach to deterministically quantify the link between physical behavior and tissue mechanobiology, inspired by integration of biomedical technologies (i.e., wearable devices, contemporary motion capture, and medical imaging) coupled to computational models of joints and musculoskeletal tissues.

Wearable body sensors and systems for “Quantified-Self” are set to transform how people interact with their environment and may facilitate personalized training and rehabilitation programs in the future. Biofeedback is a psychophysical process to augment awareness of afferent signals from sensory receptors in the human body. In the case of musculoskeletal tissues, biofeedback can be used to increase awareness and modify physical behavior (Sigrist et al., 2013). However, current rehabilitation and training protocols which incorporate biofeedback to modulate physical behavior target external biomechanics, such as the knee adduction moments (Barrios et al., 2010; Shull et al., 2011, 2013a,b; Wheeler et al., 2011) or gait spatiotemporal parameters (Wrigley et al., 2009; Erhart-Hledik et al., 2017). Eternal biomechanics are readily measured or calculated, and thus viable for use in biofeedback paradigms. Unfortunately, external biomechanics have tenuous relationships with internal biomechanics, such as articular contact loads (Walter et al., 2010; Winby et al., 2013; Saxby et al., 2016b).

Musculoskeletal tissue stresses and strains are potentially superior to external biomechanics for use in biofeedback paradigms because they are physically coupled to the processes of mechanotransduction, whereby mechanical signals are registered as biologic stimuli, and result in cell- and tissue-level adaptations controlled by biologic regulatory mechanisms. However, musculoskeletal tissue stresses and strains have not been used in biofeedback technologies, because their computation is non-trivial, and depends on a complex interplay of multiple factors, including external biomechanics, neural control, tissue morphology and micro-architecture, and material properties (Figure 1). Importantly, recent advances in neuromusculoskeletal modeling have enabled real-time prediction of whole-body kinematics and external loading (Pizzolato et al., 2017a), as well as musculoskeletal tissue loading, such as muscle-tendon unit and articular contact forces during walking gait (Pizzolato et al., 2017b). Real-time musculoskeletal modeling can now be coupled to models of internal tissue mechanics and mechanobiology, and used to provide feedback to target training for tissue strengthening and repair.

In this narrative review we present an overview of (1) the known mechanical stimuli for promoting positive tissue adaptation in musculoskeletal tissues, (2) how local tissue stresses and strains can be estimated using computational methods, (3) an approach to estimating musculoskeletal tissue stresses/strains in real-time, and (4) challenges and future directions for research in this area.

Mechanobiology is the study of the effect of mechanical stimuli on tissue biology. It is well-established that mechanical loading plays an essential role in (1) musculoskeletal tissue development throughout human maturation (Carter, 1987; Carter and Wong, 1988a,b, 1990; Wong and Carter, 1990; Carter et al., 1998, 2004; Beaupre et al., 2000), (2) maintenance of mature structures (Frost, 1988, 1990a,b,c,d), and (3) healing following injury, e.g., bone fracture (Pivonka and Dunstan, 2012). In particular, musculoskeletal tissues, such as articular cartilage, tendon, and bone, respond to strains by modulating tissue composition and organization. Generally, strains depend on the nature of applied loading, i.e., magnitude, location, orientation, duration, and frequency, as well as structural state of the object, i.e., morphology and material properties (Figure 1). Important to our study of musculoskeletal tissues, identical loads applied to different tissues (e.g., cartilage vs. bone vs. tendon), or same tissues but of different structural features (e.g., healthy vs. compromised, developing vs. mature), will produce different strains and eventually different biologic responses. Thus, to develop therapies targeting positive musculoskeletal tissue adaptations we must quantify relevant states. Equally important, if we wish evaluate therapeutic effectiveness we must also quantify changes to tissue states in response to those interventions.

Musculoskeletal tissue state varies between individuals, and is affected by disease processes. To personalize therapy, we must account for subject-specificity, such that training programs can be tailored to the individual. A further technical challenge is that we need to estimate musculoskeletal tissue mechanics in real-time, providing an appropriate form of biofeedback to enable individuals to volitionally modulate tissue mechanics during rehabilitation, recreation, or daily activities. To achieve this goal we must merge whole-body representations of human physical behavior with models of musculoskeletal tissue mechanics and mechanobiology within efficient computational frameworks.

Currently, there is no feasible method to directly measure in vivo loading applied to, and stresses/strains within, musculoskeletal tissues in native human joints. Articular contact forces can be measured in cadavers through a combination of robotic control and mathematical modeling (Wang et al., 2014) or by inserting pressure sensitive film between articulating surfaces (Ihn et al., 1993). However, a valid method of applying physiologic muscle, body, and inertial loads to cadavers has not been reported, thus casting doubt whether these measurements are representative of in vivo loading. Contact forces can also be measured by instrumenting prostheses used in arthroplasty, as has been done at knee (D'lima et al., 2005, 2006; Heinlein et al., 2007, 2009; Fregly et al., 2012), hip (Rydell, 1966; English and Kilvington, 1979; Bergmann et al., 2010), and shoulder (Bergmann et al., 2007) joints. Contact loads measured by instrumented prostheses provide critical information to implant designers regarding the nature of the mechanical demands placed upon these devices. Unfortunately, instrumented prosthetic implants are only appropriate for measuring contact loads in arthroplasty patients, who are typically elderly individuals with substantially degenerated joints and peri-articular muscle atrophy. Furthermore, arthroplasty, by definition, does not preserve the native joint and restricts the activity types that could be studied in these patients, e.g., it is unethical to ask an elderly knee arthroplasty patient to perform vigorous athletic movements. Consequently, contact loads sustained by implants are unlikely to be representative of contact loads in native joints of young physically active populations.

In addition to articular contact forces, muscle-tendon unit forces have also been directly measured in both animals (Walmsley et al., 1978; Hodgson, 1983; Herzog et al., 1992) and humans (Gregor et al., 1987; Komi et al., 1987; Fukashiro et al., 1995) by surgically implanting mechanical gauges. Proficient surgical implantation results in minimal inflammatory response, and instruments may left in situ in animals for days or even weeks. However, extrapolating in vivo animal muscle-tendon force measurements to humans is questionable and certainly of limited clinical relevance. In humans, surgical implantation of strain gauges into tendon may affect an individual's physical behavior, thus limiting ecologic validity of the measurements. Furthermore, muscle-tendon forces are subject-, task-, and state-specific, thus limiting applicability of measurements from an individual performing a specific task to another individual, movement or control task, or the same individual at a later date, i.e., following an intervention, suffering an injury, or onset of disease. Given the serious limitations of direct measurement of musculoskeletal tissue loads, researchers have used computational neuromusculoskeletal to predict musculoskeletal tissue loading.

Behavioral movement changes, in the form of modulation of body kinematics and kinetics, have been used in rehabilitation to assist motor learning and improve function following injury or disease (Sigrist et al., 2013). Much research has focused on biofeedback technologies to improve movement and function in knee osteoarthritis patients (Barrios et al., 2010; Shull et al., 2013a,b; Van Den Noort et al., 2015). In these patients, larger magnitude walking knee adduction moments have been associated with structural progression of knee osteoarthritis (i.e., joint space narrowing; Miyazaki et al., 2002) and knee pain (Amin et al., 2004), making reduction of the magnitude of the knee adduction moment a logical target for physical therapy.

Numerous studies have combined biofeedback technologies with gait modification strategies to modify joint kinematics or external loads with the aim of improving health outcomes or reducing movement variability. For example, real-time visual biofeedback of upper-body posture (Hunt et al., 2011) and dynamic knee alignment (Barrios et al., 2010) have been used to reduce walking knee adduction moments in healthy individuals and knee osteoarthritis patients, respectively. Knee braces instrumented with auditory feedback have been used to reduce knee loading rates during walking (Riskowski et al., 2009), while instrumented footwear has been used to reduce lateral foot pressures through vibrotactile feedback (Dowling et al., 2010). Notably, Shull et al. (2013b) provided haptic feedback, delivered through body worn vibrating motors, to inform participants of their changes in foot progression and trunk sway during treadmill walking. This resulted in patients with knee osteoarthritis reducing their peak knee adduction moment magnitudes. However, the main limitation of modifying external kinematics and kinetics is their tenuous relationships with internal loads (Walter et al., 2010, 2015; Winby et al., 2013; Saxby et al., 2016b), which implies weaker still relationships to articular tissue stresses and strains. The reason for these poor relationships is external biomechanics cannot account for the direct effect of muscles on musculoskeletal tissue loading (Walter et al., 2010; Winby et al., 2013; Saxby et al., 2016b).

As previously discussed, neuromusculoskeletal models can provide FEM with appropriate boundary conditions to estimate musculoskeletal tissue stresses and strains (Besier et al., 2005, 2009; Fernandez et al., 2014). This may be done in an offline analysis, but real-time estimation of musculoskeletal tissue stresses and strains requires interfacing with, and enabling data flow from, external devices to modeling software to complete necessary computations within given time constraints. For neuromusculoskeletal models, this means solving kinematics, kinetics, and muscletendon forces in real-time. Muscle forces have been calculated in real-time using a static optimization method (van den Bogert et al., 2013), where an algorithm determined the minimized weighted sum of muscle forces that matched external joint moments (Van Der Helm, 1994). However, the real-time approach presented by (van den Bogert et al., 2013) was based on a generic anatomic model that could not be personalized. Model personalization, noted earlier in this review, is essential when coupling neuromusculoskeletal and FEM models of musculoskeletal tissue mechanics. Furthermore, many neuromusculoskeletal models rely on mechanical optimization to solve the muscle redundancy problem (Crowninshield, 1979; Crowninshield and Brand, 1981), however, mechanical optimization methods struggle to predict many empirical features of muscle coordination, such as patterns of muscle activation (Herzog and Binding, 1992), co-contraction (Herzog and Binding, 1993), and force sharing (Binding et al., 2000). To our knowledge, the first use of a real-time EMG-informed neuromusculoskeletal model was by Manal et al. (2002), and first applied to musculoskeletal tissue loading in Achilles tendon rehabilitation by Manal et al. (2012). These papers advanced the field and should be acknowledged as pioneering, but were limited in application to quasi-static movements and a single joint with few degrees of freedom. Recently, Pizzolato et al. (2017b) developed software, based on OpenSim (Delp et al., 2007), to calculate full-body kinematics and kinetics (Pizzolato et al., 2017a), as well as musculoskeletal tissue loading (Pizzolato et al., 2015, 2017b), in real-time. Their method is fully extensible to other joints and musculoskeletal tissues, but is currently limited to expensive and immobile laboratory-based stereophotogrammetry systems (Pizzolato et al., 2017b).

Wearable sensors that accurately estimate human kinematics are a promising alternative to laboratory-based measurement systems. Linear accelerometers have been used for many years to quantify movement patterns relative to the gravitational field and ambulatory temporal-spatial parameters (Kavanagh and Menz, 2008), but their estimates of joint kinematics are limited by signal drift caused by integration errors (Djuric-Jovicic et al., 2011). Improvements in microelectromechanical systems have enabled embedding tri-axial accelerometer, gyroscope, and magnetometer into a single sensor. These integrated sensors are known as inertial measurement units and are able to estimate spatial orientation (Sabatini, 2006; Madgwick et al., 2011) and, when used in combination with anatomic models, joint angles. Strain sensors are another class of promising wearable sensors that can be used to estimate joint angles (Nakamoto et al., 2016). Strain sensors are low profile, flexible, and can be easily embedded into garments or mounted on the skin (Amjadi et al., 2016). To date, strain sensors have been used in biomechanics primarily to classify movement (Mattmann et al., 2007) or estimate single joints angles (Nakamoto et al., 2016). However, continuous technologic improvements in smart textiles (Honarvar and Latifi, 2017) may soon lead to advanced garments capable of estimating full-body kinematics.

Measuring or estimating reaction forces between body and ground is required to correctly estimate load applied to specific musculoskeletal structures, such as joints and ligaments. In laboratory conditions, ground reaction forces are acquired via ground mounted force plates, but alternative solutions are required for applications in the real-world. Pressure-sensitive insoles can be used to estimate the normal component of the ground reaction force, but they neglect shear components (Chesnin et al., 2000). Conversely, shoes instrumented with tri-axial force sensors have shown agreement with force plates for all components of the ground reaction force vector (Liedtke et al., 2007). Alternative to measurements, deep learning algorithms have been shown to correctly estimate ground reaction forces during walking (Oh et al., 2013). However, these data-driven models require big data as training sets. Mechanical approaches can be used to solve dynamics of motion and estimate ground reaction forces without body-worn force sensors. For example, the zero-point moment is an algorithm developed for humanoid robots (Xiang et al., 2009) that has also been successfully applied human biomechanics (Fluit et al., 2014; Dijkstra and Gutierrez-Farewik, 2015). However, to correctly estimate ground reaction forces, full-body kinematics and subject-specific musculoskeletal models are required (Fluit et al., 2014).

Overall, advances in wearable sensors, i.e., smaller, lighter, low-power, and integrated sensor systems, will enable novel real-world applications (Brodie et al., 2008). Currently, intrinsic limitations and measurement inaccuracies associated with these devices prevent their use in advanced biomechanical analysis. Combining wearable sensors with sophisticated biomechanical models may help to minimize the limitations associated with wearable sensors. Realistic musculoskeletal models, such as those offered by OpenSim (Delp et al., 2007), associated with probabilistic frameworks that adequately model wearable sensor inaccuracies (Latella et al., 2016) and computationally efficient real-time software architectures (Pizzolato et al., 2017a,b), have the potential to accurately estimate human motion in real-world setting free from the laboratory.

As previously stated, boundary conditions for subsequent FEM model simulations may be computed in real-time using neuromusculoskeletal models. However, even if appropriate boundary conditions are provided, real-time solutions to continuum mechanics problems is an ongoing computational challenge. When implementing entire musculoskeletal structures in FEM models (e.g., complete bones), computational demand may be substantially reduced by spatially averaging many microstructural features, such as trabecular and cortical bone architecture. However, spatial averaging neglects analysis of tissue anisotropy and micro-architecture, which are known to influence tissue function (Stein et al., 2010). Generally, FEM models are computationally demanding and not solvable in real-time. Thus, FEM models must be reduced to surrogates by a process known as “Kriging” (Matheron, 1963), whereby the continuum model is first solved offline for all possible, or physiologic, configurations (Wu et al., 2014; Eskinazi and Fregly, 2016), and simulation results are then be stored for future real-time use. However, it is computationally expensive to establish robust surrogates of musculoskeletal tissue continuum models, given the large data throughput imposed by performing many multi-scale simulations (Erdemir et al., 2015). One potential strategy we are pursuing is use of high-performance computing, whereby a large number of simulations are managed and solved by a remote computing cluster.

Our proposed framework to modify an individual's physical behavior to optimize musculoskeletal tissue mechanobiology (Figure 3) is feasible and currently being developed. In Table 1 we have summarized several challenges and possible future directions discussed in the text. To move these bioinspired technologies to clinical settings we need to direct our efforts toward: (1) rapid generation and seamless integration of personalized neuromusculoskeletal and FEM models, (2) use of wearable sensors, and (3) improvement of biofeedback modalities for stress and strain modulation.

Currently, creating personalized anatomic models from medical imaging is expensive (i.e., involves costly image acquisition and numerous man-hours to process raw medical imaging into high-fidelity computational models). However, improvements in image processing software, such as automatic segmentation and statistical shape modeling (Zhang et al., 2014; Zhang and Besier, 2017) may greatly accelerate model generation. Statistical shape modeling is promising as it may limit the need to acquire expensive medical imaging, relying instead upon a musculoskeletal atlas database to characterize an individual's anatomy from sparse or meta-data (Zhang and Besier, 2017). However, it is unclear whether current publically available medical imaging databases are sufficient to represent the variability in musculoskeletal anatomy in clinical populations, or those with traumas or implants. This is a limitation that will eventually be addressed by data sharing amongst research teams, which is an effort we thoroughly support.

Another limitation is that motion capture systems typically used in research gait laboratories are seldom used into clinical settings, because of their complexity, space requirements, and high purchase and operational costs. For bioinspired technologies to be broadly adopted, we need to free ourselves of traditional motion capture systems and look to wearable sensors to measure movement, external loads, and muscle excitation. A promising and relatively inexpensive example of wearable sensors that could help us on this mission are inertial measurement units, which provide a wealth of data that may be used to determine whole-body motion. Currently, inertial measurement units are limited by issues such as insufficient shielding from electromagnetic interference (i.e., while walking on treadmills or near informatics cabling) and registration of body-worn sensor positions to anatomic models. Wireless EMG systems have been used effectively in research and clinical settings for many years, and are now being integrated with inertial measurement units and other sensors as standalone devices or embedded into garments. Future research should aim to first establish if these wearable sensor systems can match the performance of traditional motion capture systems, and then minimize the number of sensors required to drive neuromusculoskeletal models.

Very little is currently known about the ability of individuals to volitionally modulate musculoskeletal tissue stresses and strains in response to real-time feedback. To our knowledge, musculoskeletal tissue stresses and strains have never been estimated in real-time, let alone used to modify physical behavior. To date, only two research groups (Manal et al., 2012; Pizzolato et al., 2017b) have provided real-time biofeedback of musculoskeletal tissue loads, but their work has been limited to muscle-tendon and rigid articular contact forces, and did not model tissue stresses and strains.

We know from previous studies people can use visual biofeedback to manipulate external biomechanical variables, muscle excitations, and tibiofemoral contact forces (Manal et al., 2012; Pizzolato et al., 2017b). Future research should strive to identify the biofeedback modality optimal for modulating musculoskeletal tissue stresses and strains through changes in human movement and muscle activation. Further, it may be possible to draw inspiration from a variety of native mechanoreceptors in the human body to provide enhanced visual biofeedback of stresses and strains. We imagine a technology whereby estimates of musculoskeletal tissue loading (i.e., forces or stresses and strains) could be transformed according to golgi organelle and muscle spindle transfer functions to provide more intuitive biofeedback.

By optimizing the mechanical environment it may be possible to regulate musculoskeletal tissue mechanobiology, potentially preventing disease, or restoring degraded tissue to health. Consequently, modeling and controlling physical behavior of individuals has enormous implications for development and management of chronic musculoskeletal diseases such as osteoarthritis or tendinopathies. We have presented a framework to move from in vitro and ex vivo studies of tissue mechanobiology to personalized in silico real-time models of musculoskeletal tissue loading. Integrating and translating these bioinspired technologies to clinical settings will prove challenging and resource intensive. Skepticism from clinicians accustomed to generic recommendations based on linear statistics is anticipated and will need to be overcome by demonstrating the efficacy and clinical utility of the proposed new approach. However, there awaits a wide spectrum of important clinical conditions to which these bioinspired technologies could be applied with the goal of reducing the socio-economic burden of musculoskeletal diseases.

CP, DL, and DS contributed to conceptualize, draft, critically revise, and approve the final version of the article. RB, JC, MZ, and TB contributed to conceptualize, critically revise, and approve the final version of the article.