## 4 Discussion  
The purposes of this study were to: (1) quantitatively determine the number and location of wearable sensors required on trunk for characterizing senior falls using a 15-segment, full-body biomechanical model; (2) examine and compare kinematic differences between daily activities and fall situations; and (3) identify the fall direction in order to minimize the desired air-pad system. The frequency of falls and fall-related injuries among seniors are becoming a major problem worldwide. The majority of previous researches have tended to focus on prevention related to balance ability of seniors and what causes a fall to occur [7; 17; 22], but what if the inevitable does take place? It is essential to determine fall characteristics and how they differ from that of normal activities. The current state of such studies is well documented by [PERSON]'s recent review article, which examined 96 selected studies from numinous electronic databases [23]. Published evidence-based support for commercially available fall detection devices is still lacking. A worldwide study is needed to address fundamental issues such as incident verification and fall quantification. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Duration** & **Fall-Forward** & **Fall-Back** & **Fall-Down** & **Fall-Left** & **Fall-Right** & **Trip-Fall** \\ \hline
**Fall Time (s)** & 0.92 \(\pm\) 0.21 & 0.79 \(\pm\) 0.18 & 0.72 \(\pm\) 0.16 & 0.94 \(\pm\) 0.25 & 0.93 \(\pm\) 0.23 & 0.76 \(\pm\) 0.13 \\ \hline \end{tabular}
\end{table} 
Table 1: The duration (mean \(\pm\) standard deviation) of fall at each fall situations \begin{table}
\begin{tabular}{c c c} \hline
**Marker** & **Percentage of Time Elapsed at Max Speed** & **Percentage of Time Elapsed at 2.1 m/s** \\ \hline
**C7** & 90.2 \(\pm\) 0.5 & 58.2 \(\pm\) 0.3 \\
**T10** & 88.8 \(\pm\) 0.4 & 60.3 \(\pm\) 0.5 \\
**Clavicle** & 86.5 \(\pm\) 0.6 & 59.3 \(\pm\) 0.3 \\
**Sternum** & 89.7 \(\pm\) 0.4 & 60.4 \(\pm\) 0.2 \\
**LSho** & 89.3 \(\pm\) 0.3 & 57.9 \(\pm\) 0.6 \\
**Rsho** & 89.6 \(\pm\) 0.3 & 57.7 \(\pm\) 0.2 \\
**LB** & 89.8 \(\pm\) 0.4 & 58.9 \(\pm\) 0.3 \\
**RB** & 88.9 \(\pm\) 0.3 & 59.2 \(\pm\) 0.4 \\
**LLB** & 87.2 \(\pm\) 0.2 & 57.7 \(\pm\) 0.3 \\
**LRB** & 87.4 \(\pm\) 0.4 & 57.5 \(\pm\) 0.5 \\ \hline \end{tabular}
\end{table} 
Table 2: Timing characteristics of the markers of interest during fallsIn the current study, low reliability was found for using max acceleration reached during a fall to detect a loss of balance. The results somehow match the divergence results among previous studies [23], which suggest that the raw signals from accelerometers may not be applied directly. An algorithm for post-data processing should be developed. Highly significant differences of max speeds were found between the fall situations and the normal activities. The results of the current study suggest that fall detection could be reliably achievable. Since speed is an overtime effect of acceleration (integration of acceleration over time), acceleration sensors could be used to develop a sensory-trigger device. A vest containing such wearable sensors would detect fall activity and inflate mini-airbag system in the event of a fall. The concern of using max speed is that the maximum occurs too late in the event of a fall (\(\sim\)90% of the duration), minimal time would be allocated for airbag deployment. For overcoming the methodological pitfall, a threshold value (i.e., 2.1 m/s) could be applied. The current study has revealed that the value would indicate a high likelihood that a fall was taking place. The difference in time between the beginning of a fall and the reaching of the threshold is between 0.2 and 0.3 s (Tables 1 and 2). This time span is long enough for inflating an airbag (i.e., less than 0.1 s needed) [24]. For more reliability, the threshold value could even be set at 2.5 m/s without change of a full inflation. The number and locations found to reach the highest sensibility of detection were to position two sensors on the left and right shoulder (LSho and RSho).