Estimation of the knee joint load using plantar pressure data measured by smart socks: A feasibility study

BACKGROUND

Unsupervised sports activities could cause traumas, about 70% of them are those of the low extremities. To avoid traumas, the athlete should be aware of dangerous forces acting within low extremity joints. Research in gait analysis indicated that plantar pressure alteration rate correlates with the gait pace. Thus, the changes in plantar pressure should correlate with the accelerations of extremities, and with the forces, acting in the joints. Smart socks provide a budget solution for the measurement of plantar pressure.

OBJECTIVE

To estimate the correlation between the plantar pressure, measured using smart socks, and forces, acting in the joints of the lower extremities.

METHODS

The research is case study based. The volunteer performed a set of squats. The arbitrary plantar pressure-related data were obtained using originally developed smart socks with embedded knitted pressure sensors. Simultaneously, the lower extremity motion data were recorded using two inertial measurement units, attached to the tight and the ankle, from which the forces acted in the knee joint were estimated. The simplest possible model of knee joint mechanics was used to estimate force.

RESULTS

The estimates of the plantar pressure and knee joint forces demonstrate a strong correlation (r= 0.75, P< 0.001). The established linear regression equation enables the calculation of the knee joint force with an uncertainty of 22% using the plantar pressure estimate. The accuracy of the classification of the joint force as excessive, i.e., being more than 90% of the maximal force, was 82%.

CONCLUSION

The results demonstrate the feasibility of the smart socks for the estimation of the forces in the knee joints. Smart socks therefore could be used to develop excessive joint force alert devices, that could replace less convenient inertial sensors.

Classifying Diverse Physical Activities Using "Smart Garments"

Physical activities can have important impacts on human health. For example, a physically active lifestyle, which is one of the most important goals for overall health promotion, can diminish the risk for a range of physical disorders, as well as reducing health-related expenditures. Thus, a long-term goal is to detect different physical activities, and an important initial step toward this goal is the ability to classify such activities. A recent and promising technology to discriminate among diverse physical activities is the smart textile system (STS), which is becoming increasingly accepted as a low-cost activity monitoring tool for health promotion. Accordingly, our primary aim was to assess the feasibility and accuracy of using a novel STS to classify physical activities. Eleven participants completed a lab-based experiment to evaluate the accuracy of an STS that featured a smart undershirt (SUS) and commercially available smart socks (SSs) in discriminating several basic postures (sitting, standing, and lying down), as well as diverse activities requiring participants to walk and run at different speeds. We trained three classification methods—K-nearest neighbor, linear discriminant analysis, and artificial neural network—using data from each smart garment separately and in combination. Overall classification performance (global accuracy) was ~98%, which suggests that the STS was effective for discriminating diverse physical activities. We conclude that, overall, smart garments represent a promising area of research and a potential alternative for discriminating a range of physical activities, which can have positive implications for health promotion.

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.

Relationship between skin temperature monitoring with Smart Socks and plantar pressure distribution: a pilot study

OBJECTIVE

Increased skin temperature at the plantar aspect of the foot can predict foot ulceration. However its relation to plantar pressure overload is unknown. The aim of this study was to assess the ability of 'smart socks', monitoring plantar temperature under real-life conditions, to predict plantar pressure distribution.

METHOD

The 'smart socks' have seven thermal sensors woven into the fabric of the sock to measure the temperature beneath the foot in real-life conditions. The upper part of the sock is connected to a central unit through which changes in the sensor resistance is converted into temperature changes. Participants were instructed to wear the socks for three continuous hours. Plantar pressure was measured by the MatScan plantar-pressure measurement system (Tekscan Inc., US).

RESULTS

The study included 25 healthy volunteers (11 males, 14 females, mean age was 41.1 years (standard deviation (SD): 17.6) years, a mean body mass index of 29.4 kg/m² (SD: 6.95). Temperature changes at sensor (S) five significantly correlated with metatarsal (M) 2 pressure time integral (PTI) (r=0.519, p=0.008), M3 PTI (r=0.435, p=0.03), M4 PTI (r=0.452, p=0.023). Changes at S5 also significantly correlated with peak pressure at M2 (r=0.66, p=0.000), M3 (r=0.52, p=0.01), and M4 (r=0.60, p=0.002). Temperature changes at S6 were significantly correlated with changes at S1, S2, S3, S4, S5, and S7.

CONCLUSION

Temperature changes at the plantar aspect of the foot measured by the smart socks are correlated with plantar pressure distribution. Furthermore, two sensors at positions S5 and S6 were sufficient to predict plantar pressure changes.

Preferred Placement and Usability of a Smart Textile System vs. Inertial Measurement Units for Activity Monitoring

Wearable sensors and systems have become increasingly popular in recent years. Two prominent wearable technologies for human activity monitoring are smart textile systems (STSs) and inertial measurement units (IMUs). Despite ongoing advances in both, the usability aspects of these devices require further investigation, especially to facilitate future use. In this study, 18 participants evaluate the preferred placement and usability of two STSs, along with a comparison to a commercial IMU system. These evaluations are completed after participants engaged in a range of activities (e.g., sitting, standing, walking, and running), during which they wear two representatives of smart textile systems: (1) a custom smart undershirt (SUS) and commercial smart socks; and (2) a commercial whole-body IMU system. We first analyze responses regarding the usability of the STS, and subsequently compared these results to those for the IMU system. Participants identify a short-sleeved shirt as their preferred activity monitor. In additional, the SUS in combination with the smart socks is rated superior to the IMU system in several aspects of usability. As reported herein, STSs show promise for future applications in human activity monitoring in terms of usability.