Assessing the risk of fall in elderly people is a difficult challenge for clinicians. Since falls represent one of the first causes of death in such people, numerous clinical tests have been created and validated over the past thirty years to ascertain the risk of falls. More recently, the developments of low-cost motion capture sensors have facilitated observations of gait differences between fallers and non-fallers. The aim of this study is twofold. First to design a method combining clinical tests and motion capture sensors in order to optimize the prediction of the risk of fall. Second to assess the ability of an artificial intelligence to predict risk of fall from sensor raw data only. Seventy-three nursing home residents over the age of 65 underwent the Timed Up and Go (TUG) and a 6 minutes walking tests equipped with a home-designed wearable Inertial Measurement Unit during two sets of measurements at 6 months interval. Observed falls during that interval enabled us to divide residents into two categories: fallers and non-fallers. We show that the TUG test results coupled to gait variability indicators, measured during a 6 minutes walking test, improves (from 68% to 76%) the accuracy of risk of fall’s prediction at six months. In addition, we show that artificial intelligence algorithm trained on the sensor raw data of fifty-seven participants reveals an accuracy of 75 % on the remaining sixteen participants. Keywords: Risk of fall, Elderly, Wearable sensor, Gait variability, Clinical tests 1. Introduction Falls are an inevitable part of ageing and their prediction and prevention is of paramount importance to health care. According to the World Health Organization (WHO), falls are the second cause of accidental death and approximately 646000 people die every year following falls, particularly people over the age of 65. In the European Union (EU), an average of 35848 elderly people per year (65 and above) are reported to have died from falls [1]. As the population of elderly people in the EU is expected to grow by 60% by 2050, the number of fall-related deaths is expected to increase to almost 60000 per year by 2050, unless additional measures are taken to predict and prevent falls. Additionally, falls result in significant physical and psychosocial costs that have to be incurred by patients and social security. The social security cost for treating fall-related injuries in the EU is estimated to be 25 billion € each year [2]. In nursing homes, falls frequency is higher for elderly people than at home with a yearly rate of falls of 50% [3]. This phenomenon is foreseen to increase in the future in view of the population aging. Risk factors for falls in elderly people are higher among nursing home residents compared to home-based individuals [4]. Those factors are usually grouped in three main categories: intrinsic (age, sex, cognitive disorders, eye-sight impairment, sarcopenia, multiple medication use, sensory impairment, gait disorders, postural instability and neurodegenerative diseases), behavioral (inappropriate footwear, alcohol use), and environmental (slippery surfaces, poor lighting, worn carpeting) [3,4,5]. It is well known that the assessment of the risk of falls is better achieved through screening tools designed for that purpose. One of the most well-known test is the Timed Up and Go (TUG) test [6]. It is considered as the gold standard in fall risk assessment and has numerous advantages. It is simple and easy to describe and to perform, and therefore widely used [7]. It is also recommended by the American Geriatric Society and the British Geriatric Society [8]. The typical time to perform the test is around 20-30 seconds. Unfortunately, this is too short to enable a full assessment of the gait kinematics and more specifically the long-term variability of the stride interval. This is correlated to the risk of fall in home-based older people [9] but requires time series of typical duration of 10 minutes. More recently, TUG test has been performed with patients equipped with an accelerometer. This allowed an analysis focusing on sit-to-stand and stand-to-sit subtasks, which is useful to identify fall risk in home-based fallers versus healthy controls [10]. The findings of this study show that the TUG test allows to identify successfully 63% of fallers and this is increased up to 87% in accelerometer-equipped patients. Another widely used tool in clinical practice that allows the study of gait for a longer period is the 6 minutes walking test [11]. Similar to the TUG test, accelerometer-based 6 minutes walking test has been used in patients with chronic heart failure [12] and chronic obstructive pulmonary disease [13]. To the best of our knowledge, accelerometer-based 6 minutes walking test has not been performed in elderly people to assess risk of fall. Today, the availability of low-cost inertial sensors enables 3D measurement of acceleration and angular speed during human movements. In a recent study, we successfully used an ultra-low-cost wearable inertial sensor to capture rotational movements of the head during a standardized test [14]. Here, we used this sensor to capture locomotor movements during the 6 minutes walking test to increase its predictive power of the TUG test on the risk of falls among elderly nursing home fallers and non-fallers during two sets of measurements at 6 months interval. We present an analysis of kinematic data based on variability assessment and on artificial intelligence (AI) techniques. Current hardware capacities allow to process and manage the huge datasets required for such tools to converge to a solution. That is why these techniques have gained popularity in recent years and are recognized as efficient tools in fall detection in particular [15]. 2. Materials and Methods 2.1. Population Participants to this survey were at least 65 years old or over and lived in 4 nursing homes in the Charleroi area in Belgium. The experiment protocol was designed according to the Helsinki declaration and was approved by the Bioethical Academic Committee (n°B200-2017-144). Exclusion criteria were as follows: lower limb musculoskeletal or cardio-respiratory disorders preventing a 6 minutes walking test or major cognitive disorders preventing a full patient’s cooperation. During the experiment, participants who experienced a major illness such as stroke or limb fracture were excluded. Participants who chose to discontinue their participation, who were admitted to hospital, who had their medication changed thus preventing the continuation of the experiment or who died before the completion of the experiment were also excluded. Ninety-two individuals started the initial test. Twelve of them were excluded from the survey in view of the above criteria. Seventy-three participants completed the experiment. Table 1. Main characteristics, comorbidities and medications increasing the risk of fall of the participants. Fallers have been identified according to the fall records between the 6 months interval (t1 and t2). t1 t2 N 80 73 Age (years) 83.2±8.2 83.0±8.3 Male/Female 28/52 28/45 Walking aid required 49 52 Hypertension (%) 44 42 Medications 4 4 Cerebrovascular accident (%) 10 10 Dementia (%) 14 16 Previous heart surgery (%) 21 23 Diabetes (%) 16 15 Hip or knee replacement (%) 16 16 Fallers 23 Timed Up and Go (s) 20 [17-27] Age is indicated under the form mean±standard deviation. Medications increasing the risk of fall: Psychotrope, antiarythmic, diuretics [16]. Timed Up and Go (TUG) results are given under the form median [1st quartile-3rd quartile]. Hypertension is defined as a value >140/90 mmHg. 2.2. Protocol The survey took place between the month of May (t1) and the month of November (t2) 2018. At t1, TUG test was performed in all participants. Participants sat down with their back against a 46 cm high chair rest. When signaled, participants were asked to get up, walk three meters, turn round and sit down again. Participants then performed the 6 minutes walking test with the right to take short pauses as required. Measurements were taken by the two same experimenters (L.C. and R.G.), who always walked aside the patient to prevent any fall in case they lost balance. Six minutes tests were performed by walking a typical distance of 25 m with serial turnarounds. During the 6 minutes walking test, participants were equipped with a homemade sensor collecting several kinematic data. The ultra-low-cost sensor, called DYSKIMOT, was presented in a previous survey [14]. It is based on the Magnetic Angular Rate and Gravity (MARG) sensor LSM9DS1 (SparkFun), composed of 3-axis accelerometer, gyrometer and magnetometer, plus a temperature sensor (Figure 1A and 1B). It is light (10.44 g) and small enough (3 × 3 cm) to be worn by a patient without any disturbance (Figure 1B). Among other quantities, the MARG sensor measures acceleration, 𝒂""⃗(𝒕) (in [g], ±16 [g]), and angular velocity, 𝛚"""⃗(𝒕) (in [°/s], ±2000 [°/s]) at a sampling frequency of 100 Hz. The data are transmitted to a PC via an Arduino Uno Rev 3 and a USB cable (RS232 serial link) and then transferred to a home-made acquisition software for further analysis. More details can be found in [14], including a comparison between DYSKIMOT and a gold-standard optoelectronic sensor. This comparison shows that the DYSKIMOT’s accuracy is adequate in measuring head rotation during a standardized test. During the experiment, the DYSKIMOT was positioned in the lumbar area of the individual at the level of L4 vertebra (Figures 1C and 1D) in such a way that the sensor’s cartesian frame matched with walking directions. Between t1 and t2, each fall of a participant was recorded by the nursing home staff. The fall records were used at t2 to categorize participants into fallers and non-fallers. At the end of the survey, 23 fallers and 50 non-fallers were noted. Figure 1. (A) The LSM9DS1 sensor (SparkFun) used to record acceleration and angular speed. (B) LSM9DS1 sensor within the DYSKIMOT system. Sensor’s cartesian frame is indicated. The curved arrows give rotation’s positive direction. (C) Schematic placement of DYSKIMOT sensor on a patient. The sensor’s cartesian frame matches the walking directions. (D) A patient equipped with DYSKIMOT sensor (right) with one experimenter (R.G.) walking aside. Figure 2. (A) Typical traces of antero-posterior and lateral accelerations measured by the DYSKIMOT during the 6 minutes walking test. (B) Typical trace of vertical angular speeds measured by the DYSKIMOT during the 6 minutes walking test. The regularly spaced peaks correspond to turnarounds made by the participants. 2.3. Data analysis Typical traces of the time series included in 𝛚"""⃗=(𝝎𝒎𝒍,𝝎𝒗,𝝎𝒂𝒑) and 𝒂""⃗=(𝒂𝒎𝒍,𝒂𝒗,𝒂𝒂𝒑) (2 times 3 components) are shown in Figure 2. The indices ml, v and ap stand for medio-lateral, vertical and antero-posterior respectively. We recorded for analysis the data at t1 and t2 of the 73 participants still included at t2. We chose to focus on the assessment of time series variability. As shown in [5], gait variability can be correlated with the risk of fall. Further studies such as [9] have analyzed it using mathematical tools such as fractal dimension and have demonstrated that those tools can discriminate between healthy and disabled individuals, healthy individuals showing a higher fractal dimension. The parameters identified in the present study are the standard deviation (SD) and fractal dimension (D) obtained from the six time series recorded. They were calculated according to the method presented in [17]. Additional mathematical