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Runners and especially long-distance runners face various overuse injuries, such as Achilles tendinopathy, shin splints or stress fractures. These overuse injuries are caused in parts by the repetitive load application on musculoskeletal structures. Otherwise, the load repartition in running is narrowly related to the foot strike pattern adopted. Three foot strike patterns are typically identified: heel strike, midfoot strike and forefoot strike. Depending the foot strike pattern adopted, the intensity and location of loads applied on the runner’s body differ and so indirectly the risk to sustain certain overuse injuries. As a consequence, identifying runner foot strike pattern profile is of interest in order to prevent injuries by giving training and/or footwear recommendations according to the runner-specific injury history. This chapter aims at presenting a simple method for determining foot strike pattern in situ and continuously. This method requires two accelerometers to record heel and forefoot accelerations. From these signals, the delay between the heel and metatarsals strikes, represented by their respective peak acceleration, is measured. From this temporal variable, the percentage of each foot strike pattern over a race or a training session is evaluated. By synchronizing accelerometers with GPS unit, this method also permit to study the interaction between fatigue, slope, surface, etc., and the foot strike pattern profile for a given runner. This simple method is of interest for fundamental research but also for clinicians, coaches or running shoe manufacturers.

As discussed in Chap. 1, slip and fall incidences are widely studied owing to their prevalence and associated costs in terms of human suffering and economic burden around the world. The incidents are categorized into falls from an elevation and same level falls. Falls from an elevation are those where the point of contact is below the level of the original supporting surface of the faller. Falls from the elevation are one of the leading causes of workplace fatalities and traumatic brain injuries on the job. These incidents create a substantial risk that an employee or worker will become momentarily or permanently disabled.

Although many causes are involved in the slip and fall incidents, footpaths and walkways have a major effect on safe ambulation of the pedestrians. Pedestrian floorsurfaces should be constructed to provide comfortable walking environments and good traction functioning against any slippery condition.

Fall incidents resulting from slips or trips are one of the most common injuries at workplaces . On average, they cause 40% of all reported major injuries and lead to other types of serious accidents such as falls from height (HSE 2011). Slips and falls are also one of the leading categories of non-traffic accidents in terms of serious injuries and fatalities, as well as one of the leading causes ofinjury-related deaths for the elderly aged 65 and over (Layne and Landen 1997; Scott 2009; Yeoh et al. 2013).

Step length (SL) is a fundamental parameter for the characterization of normal and pathologic gait. In particular, Freezing of Gait (FOG) in Parkinson’s Disease is a motor disorder associated with a markedly reduction of SL, hence this parameter has the potential to be used for FOG detection and monitoring. In this paper, we present a non-obtrusive and non-invasive architecture for the estimation of SL from trunk acceleration. We compared the reliability of our methodology with a stereophotogrammetric system, which is the gold standard for gait analysis. Preliminary experimental results on three healthy subjects are reported. Results show that the architecture could be employed to detect SL reduction prior to a FOG event.

Bipedal gait involves the entire body but some subsystems are decisive for gait while other parts of the body play complementary roles (dynamic balance, harmony of movement, etc.). We have proposed a functional specification of gait. It is based on logical expression format and takes into account only observational kinematic aspects. The specification is open enough that it can be used in other gait analysis problems (rehabilitation, sport, children, etc.).

A new generation of self-sustainable and wearable Microbial Fuel Cells (MFCs) is introduced. Two different types of energy - chemical energy found in urine and mechanical energy harvested by manual pumping - were converted to electrical energy. The wearable system is fabricated using flexible MFCs with urine used as the feedstock for the bacteria, which was pumped by a manual foot pump. The pump was developed using check valves and soft tubing. The MFC system has been assembled within a pair of socks.