Type of sensors used ranged from uniaxial accelerometers to triaxial accelerometers, gyroscopes and magnetic sensor. Full scale of accelerometers ranged from 3 g to 10 g, those of gyroscope ranged from 300–1,200 degree/second. For magnetic sensors, the full scale was 750 mGauss [12,13]. The weight of motion sensors adopted ranged from 18.2 g to 700 g, and the size ranged from 20 × 10 × 7.2 mm³ to 64 × 62 × 26 mm³. Sampling frequencies of these systems ranged from 20–800 Hz. Details of sensors used in the studies were shown in Table 1.

In most of the reviewed papers, collected data was not processed in a real time basis. Some of the systems have its own data logging system attached on the subject’s body. Portable data loggers with different types of memory cards, for example, flash memories and SD-micro cards [1,8,16,20,25,27,31] were one of the common methods for data logging. These memory cards allow handy data storage. There were also systems which required subjects to carry a hand held PC with them for data collection [34]. Hand held PC allows data collection in daily activities as they can be carried in pocket easily. However, their sizes were still not small enough for subjects performing vigorous sport motions. Wired systems which data were collected by a wired notebook PC also existed [17,30]. Bluetooth wireless communication was also adopted [9,33], which allows subjects to have more freedom of motion during data collection. However, workstation must present for data collection, therefore it is not suitable for ambulatory system.

One of the disadvantages of application of wearable sensors in human motion analysis was that noise in data collection was usually severe. Therefore, data have to be filtered before further processing. Low pass filters with cut off frequencies ranged from 15–40 Hz were adopted in various systems [2,3,6,26,29]. The cut off frequencies were chosen carefully based on the motions being performed. Butterworth filters [17,26], Kalman filters [1,10,37] and Savitzky-Golay filters [18,19] were also adopted in some other systems according to their applications and motions to be detected (Table 2). Curved fitting technique was also adopted to eliminate noise [33].

Most of the studies reviewed recruited young (age 18–40) healthy individuals as subjects. Only two studies have older subjects with average age of aged 58.7 [18,19]. Sample size ranged from one to 36. Walking and running on flat ground or treadmill were common motions being analyzed [1,2,4,8,14,16–18,21,22,24,27,29,31,34,36,37,39]. However, some other specific motions were also involved, for example, walking on difference surfaces [26], stand-sit transition [34], landing from a fall [15], tennis serve [38], rowing, cycling [20], jumping, walking downstairs, cutting, simulated sprain [17], walking upstairs [2], knee and ankle joint movement [6,11,30].

The accuracies of the motion sensing systems were mostly compared with those of video cameras or high speed optical motion analysis systems with reflexive markers, as video cameras and optical motion analysis systems were commonly used for human joint kinematics assessment nowadays.

The reviewed papers monitored the joint kinematics of ankles, knees and hips. Most of the studies simplified these joints as simple hinge joints, which assume only sagittal plane movement was allowed. However, some studies could provide detailed three dimensional descriptions for ankle, knee and hip joints. Tibial acceleration was other commonly recorded parameters for human motion analysis. Tibial acceleration can be easily obtained from accelerometer data, without complicated data processing, therefore was favorable for real time monitoring and classification of different human activities.

By analysis of lower limb joint kinematics, several applications could be done, for example: Analysis of skill level and locomotor performance of athletes or patients [24,27,38]; ambulatory measurement to monitor patients’ daily activities [7,816,29]; clinical assessment for patients [13,30]; Gait event detection and analysis [1,18,22,23,26,37,39] and identification of different daily activities, for example stair climbing, walking, running, rowing, cycling and simulated ankle sprain [2,17,20].

Fixation methods were a very important part in motion analysis using motion sensors. A good fixation method reduced the artifices of skin movement. Moreover, good fixation methods can greatly reduced the errors caused by improper alignment to anatomical axes. Velcro straps [3,30], double-side adhesive tape [39], elastic straps [2,29,18,28] and neroprene straps [11] were commonly used for fixing motion sensors on subjects’ bodies [3,15]. These tapes and straps are flexible and convenience to use. However, errors caused by skin movement can be significant. Some of the sensors were fixed on aluminum plate [4,15,29,31,39] or put inside plastic casing [6,25] first before attaching on subject’s bodies. Hard plate and casing can reduce relative skin movement and protect the sensors from damage. However, they are usually heavy and restrict subjects from normal movement. Semi-rigid belt [27] and exoskeleton [21] harness were also used for better sensor attachment, but are not convenience for long term ambulatory use. Another important note for fixation of sensor was that we have to ensure the axes of the inertial sensors aligned with the anatomical axes of the segments. Some studies applied anatomical calibration to align sensor’s axes with the axes of the body segment. Calibration devices were adopted in static trial to identify the lines connecting anatomical landmarks [32]. Static postures were also adopted to calibrate the sensors in a functional approach [9].

Some limitations still exist for lower limb human biomechanics analysis by wearable inertial motion sensors. Firstly, filtering, integration, trigonometry were involved to estimate joint angular kinematics. Therefore, high demand of hardware was needed for data processing, this made a higher cost and larger size for the data processing unit. Most importantly, complicated data processing technique means real time analysis nearly impossible in most of the studies. In some other studies, real time analysis was possible, for example Cutti [9] and his colleagues can measure real time joint kinematics, however, it was still limited to data collection in clinical setting.

Future development of joint kinematics analysis techniques for wearable inertial sensors should focus simplification of data processing algorithm would be the most challenging part. Batteries lives, fixation method, size of central processing unit were other aspects to be improved. Inertial sensors might replace video cameras and optical motion analysis systems in some human biomechanics studies as data collection could be done outside laboratory settings. The low cost of inertial sensors is obviously another advantage against optical motion analysis systems. Most importantly, it might be applied in ambulation system for real time motion classification, feedback to athletics about sports performance, monitor patients’ daily activities and even act as alarm for activation of protective mechanism when the user was in danger of sports injury.