User:LightningAndWaves/sandbox

The action of lifting in humans is performed commonly, but can be affected by various factors. Examples of these factors include whether the person lifting has lower back pain, the kind of lift they are performing, how fatigued they are, or whether they are being externally assisted in the lifting motion. Since lifting is associated with increased rates of workplace injury, much work has been done to study the low-level impact that these elements can have, as well as how machine learning can help to detect and prevent them.

Fatigue
At the muscular level, fatigue is the reduced ability of muscles to generate force. In strenuous tasks like lifting, this sort of shift in the body’s ability to move can create changes in muscle activation as well as overall motion. Fatigue onset is typically related to repeated actions taken in the same manner over a long period. Once the muscles helping to perform the action are unable to provide the same level of assistance to the body, risk of injury can increase.

In lifting, fatigue can create differences in how people move. Previous work has included correlating perceived exertion during lifting with significant changes of lifting kinematics over time. Fatigue onset has also been shown to occur more slowly when people change their lifting style over time, or at least switch up which muscle groups they use. This is an example of the repeaters-replacers hypothesis at work.

Lower back pain
Lower back pain can be caused by various factors, meaning that roughly 80% of the population will experience it within their lifetime. However, a large portion of this group stems from manual materials handling-related work. Overextending oneself, lifting with bad form, or otherwise performing a strenuous motion unsafely can vastly increase the risk of injury when lifting. To better quantify these risk types, methods have been devised of classifying workplace-required lifting motions based on their risk type.

There are a variety of effects that lower back pain can have on the motion of lifting, ranging from kinematic changes (whole-body motion) to individual muscle-level shifts. Previous work has explored the different types of lifting styles that individuals with lower back pain use compared with healthy individuals, as well as changes in their muscle activation. An interesting trend in individuals with lower back pain is that many tend to prefer slower, more precise motions that they achieve by using muscles on the back and front of their bodies at the same time. This technique can help them to avoid pain, but using a strategy that is stiffer and uses more muscles can also be more fatiguing and cause greater spinal compressive forces as a result.

Other factors
The amount of perceived weight that an object has can also affect the way that someone lifts it. Researchers have shown that deceiving people into thinking that an object is heavier or lighter can change their perceived exertion after performing a series of lifts with it, as well as muscle activity. However, changes in back muscle activity are usually restricted to the phase of lifting before a person actually picks up an object. During the lifting phase itself, the weight of that object is the dominant factor affecting back muscle activity.

Measuring lifting changes
To measure changes in lifting, there are a variety of data that researchers can collect. They include direct physiological measurements and subject-reported outcome measures. Some examples include:


 * Motion capture
 * Electromyography (EMG)
 * Force plates
 * Rating of perceived exertion
 * Pain self-efficacy

One emerging area of research is the use of machine learning to detect changes in lifting from fatigue or to distinguish different lifting styles due to the prevalence of lower back pain. These methods are useful because they can allow clinicians to see shifts in lifting that might not be immediately distinguishable to the naked eye or raw sensor measurements.